@Article{info:doi/10.2196/69113, author="M, Premikha and Goh, Khong Jit and Ng, Qiang Jing and Mutalib, Adeliza and Lim, Yang Huai", title="Impact of Acute Respiratory Infections on Medical Absenteeism Among Military Personnel: Retrospective Cohort Study", journal="JMIR Form Res", year="2025", month="Apr", day="18", volume="9", pages="e69113", keywords="respiratory infections", keywords="military", keywords="epidemiology", keywords="public health", keywords="surveillance", abstract="Background: Acute respiratory infections (ARI) are a significant challenge in military settings due to close communal living, which facilitates the rapid transmission of pathogens. A variety of respiratory pathogens contribute to ARI, each varying in prevalence, severity, and impact on organizational productivity. Understanding and mitigating the impact of ARI is critical for optimizing the health of military personnel and maintaining organizational productivity. Objective: This retrospective study of surveillance data aims to identify pathogens causing ARI among servicemen and determine which pathogens contribute most to medical absenteeism, defined as the combined duration of the issued medical certificate and light duty. Methods: From September 2023 to August 2024, anonymous nasopharyngeal swabs (BioFire FilmArray Respiratory Panel) were collected from Singapore Armed Forces servicemen presenting with ARI symptoms after a doctor's consultation at a local military camp's medical centre. The presence of fever and duration of medical certificate and light duty were self-reported by Singapore Armed Forces servicemen. Results: A total of 1095 nasopharyngeal swabs were collected, of which 608 (55.5\%) tested positive. The most common respiratory pathogen was human rhinovirus/enterovirus (HRV/HEV) in 303 (27.7\%) individuals. The highest proportions of fever were observed in servicemen with influenza (62.8\%, 27/43), SARS-CoV-2 (34.3\%, 12/35), and parainfluenza (31.6\%, 12/38). The odds of patients with influenza that have fever was 5.8 times higher than those of patients infected with HRV/HEV (95\% CI 2.95?11.40, P<.001). The median duration of medical certificate, light duty, and medical absenteeism were 0 (IQR 0), 2 (IQR 2) and 2 (IQR 0) days, respectively. The odds of patients with influenza having a medical certificate with duration ?1 day was 5.34 times higher than those in patients with HRV/HEV (95\% CI 2.63?10.88, P<.001). No significant differences in the duration of medical absenteeism were found between HRV/HEV and other pathogens. Conclusions: Compared to HRV/HEV, influenza infections were significantly associated with longer medical certificate duration. Nonetheless, there were no significant differences in the overall duration of medical absenteeism across pathogens, as servicemen infected with other pathogens were given light duty instead. These findings emphasize the need for pathogen-agnostic ARI measures. While influenza vaccinations are already mandatory for servicemen in local military camps, encouraging additional public health measures (eg, mask-wearing among symptomatic servicemen, COVID-19 vaccinations, therapeutics) can further reduce ARI incidence, minimize the duration of medical absenteeism, and mitigate the impact on organizational productivity. ", doi="10.2196/69113", url="https://formative.jmir.org/2025/1/e69113" } @Article{info:doi/10.2196/68936, author="Fundoiano-Hershcovitz, Yifat and Lee, Felix and Stanger, Catherine and Breuer Asher, Inbar and Horwitz, L. David and Manejwala, Omar and Liska, Jan and Kerr, David", title="Digital Health Intervention on Awareness of Vaccination Against Influenza Among Adults With Diabetes: Pragmatic Randomized Follow-Up Study", journal="J Med Internet Res", year="2025", month="Apr", day="10", volume="27", pages="e68936", keywords="digital health", keywords="diabetes management", keywords="influenza vaccination", keywords="flu vaccination awareness", keywords="mobile health", abstract="Background: Diabetes mellitus significantly increases the risk of severe complications from influenza, necessitating targeted vaccination efforts. Despite vaccination being the most effective preventive measure, coverage remains below the World Health Organization's targets, partly due to limited awareness among patients. This study evaluated a digital health intervention aimed at improving influenza vaccination rates among adults with diabetes. Objective: This study aimed to demonstrate the effectiveness of digital health platforms in increasing vaccination rates among people with diabetes and to emphasize the impact of tailored messaging frequency on patient engagement and health behavior change. We hypothesized that digital tools providing empirical evidence of increased health risk awareness can effectively drive preventive actions. Methods: The study leveraged the Dario (Dario Health Corp) digital health platform to retrospectively analyze data from 64,904 users with diabetes assigned by the platform into three groups: (1) Group A received previously studied monthly flu nudge messages; (2) Group B received an adapted intervention with 2-3 monthly messages; (3) Group C served as the control with no intervention. Surveys were conducted at baseline, 3 months, and 6 months to assess vaccination status, awareness of influenza risks, and recollection of educational content. Statistical analyses, including logistic regression, chi-square tests, and t tests, were used to evaluate differences between groups. Results: Out of 64,904 users, 8431 completed the surveys. Vaccination rates were 71.0\% in group A, 71.9\% in group B, and 70.5\% in group C. Group B showed significantly higher awareness of influenza risks compared with the control group odds ratio (OR; OR 1.35, 95\% CI 1.12-1.63; P=.001), while group A did not (OR 1.10, 95\% CI 0.92-1.32; P=.27). Recollection of educational content was also higher in groups A (OR 1.29, 95\% CI 1.07-1.56; P=.008) and B (OR 1.92, 95\% CI 1.59-2.33; P<.001) compared with the control. In addition, a significant correlation between awareness and vaccination rates was found only in group B ($\chi$2(df=1)=6.12, P=.01). Conclusions: The adapted digital intervention (group B) effectively increased awareness of influenza risks and recollection of educational content, which correlated with the higher trend in vaccination rates. This study demonstrates the potential of digital health tools to enhance influenza vaccination among people with diabetes by improving risk awareness and education. Further research should focus on optimizing these interventions to achieve significant improvements in vaccination uptake and overall public health outcomes. Trial Registration: ClinicalTrials.gov NCT06840236; https://clinicaltrials.gov/study/NCT06840236 ", doi="10.2196/68936", url="https://www.jmir.org/2025/1/e68936", url="http://www.ncbi.nlm.nih.gov/pubmed/40209214" } @Article{info:doi/10.2196/67050, author="Florentino, Veras Pilar Tavares and Bertoldo Junior, Juracy and Barbosa, Gouveia George Caique and Cerqueira-Silva, Thiago and Oliveira, Ara{\'u}jo Vinicius de and Garcia, Oliveira Marcio Henrique de and Penna, Oliveira Gerson and Boaventura, Viviane and Ramos, Pereira Pablo Ivan and Barral-Netto, Manoel and Marcilio, Izabel", title="Impact of Primary Health Care Data Quality on Infectious Disease Surveillance in Brazil: Case Study", journal="JMIR Public Health Surveill", year="2025", month="Feb", day="21", volume="11", pages="e67050", keywords="primary health care", keywords="data quality", keywords="infectious disease surveillance", keywords="Brazil", keywords="early warning system", abstract="Background: The increase in emerging and re-emerging infectious disease outbreaks underscores the need for robust early warning systems (EWSs) to guide mitigation and response measures. Administrative health care databases provide valuable epidemiological insights without imposing additional burdens on health services. However, these datasets are primarily collected for operational use, making data quality assessment essential to ensure an accurate interpretation of epidemiological analysis. This study focuses on the development and implementation of a data quality index (DQI) for surveillance integrated into an EWS for influenza-like illness (ILI) outbreaks using Brazil's a nationwide Primary Health Care (PHC) dataset. Objective: We aimed to evaluate the impact of data completeness and timeliness on the performance of an EWS for ILI outbreaks and establish optimal thresholds for a suitable DQI, thereby improving the accuracy of outbreak detection and supporting public health surveillance. Methods: A composite DQI was established to measure the completeness and timeliness of PHC data from the Brazilian National Information System on Primary Health Care. Completeness was defined as the proportion of weeks within an 8-week rolling window with any register of encounters. Timeliness was calculated as the interval between the date of encounter and its corresponding registry in the information system. The backfilled PHC dataset served as the gold standard to evaluate the impact of varying data quality levels from the weekly updated real-time PHC dataset on the EWS for ILI outbreaks across 5570 Brazilian municipalities from October 10, 2023, to March 10, 2024. Results: During the study period, the backfilled dataset recorded 198,335,762 ILI-related encounters, averaging 8,623,294 encounters per week. The EWS detected a median of 4 (IQR 2?5) ILI outbreak warnings per municipality using the backfilled dataset. Using the real-time dataset, 12,538 (65\%) warnings were concordant with the backfilled dataset. Our analysis revealed that 100\% completeness yielded 76.7\% concordant warnings, while 80\% timeliness resulted in at least 50\% concordant warnings. These thresholds were considered optimal for a suitable DQI. Restricting the analysis to municipalities with a suitable DQI increased concordant warnings to 80.4\%. A median of 71\% (IQR 54\%-71.9\%) of municipalities met the suitable DQI threshold weekly. Municipalities with ?60\% of weeks achieving a suitable DQI demonstrated the highest concordance between backfilled and real-time datasets, with those achieving ?80\% of weeks showing 82.3\% concordance. Conclusions: Our findings highlight the critical role of data quality in improving the EWS' performance based on PHC data for detecting ILI outbreaks. The proposed framework for real-time DQI monitoring is a practical approach and can be adapted to other surveillance systems, providing insights for similar implementations. We demonstrate that optimal completeness and timeliness of data significantly impact the EWS' ability to detect ILI outbreaks. Continuous monitoring and improvement of data quality should remain a priority to strengthen the reliability and effectiveness of surveillance systems. ", doi="10.2196/67050", url="https://publichealth.jmir.org/2025/1/e67050" } @Article{info:doi/10.2196/63881, author="Selcuk, Yesim and Kim, Eunhui and Ahn, Insung", title="InfectA-Chat, an Arabic Large Language Model for Infectious Diseases: Comparative Analysis", journal="JMIR Med Inform", year="2025", month="Feb", day="10", volume="13", pages="e63881", keywords="large language model", keywords="Arabic large language models", keywords="AceGPT", keywords="multilingual large language model", keywords="infectious disease monitoring", keywords="public health", abstract="Background: Infectious diseases have consistently been a significant concern in public health, requiring proactive measures to safeguard societal well-being. In this regard, regular monitoring activities play a crucial role in mitigating the adverse effects of diseases on society. To monitor disease trends, various organizations, such as the World Health Organization (WHO) and the European Centre for Disease Prevention and Control (ECDC), collect diverse surveillance data and make them publicly accessible. However, these platforms primarily present surveillance data in English, which creates language barriers for non--English-speaking individuals and global public health efforts to accurately observe disease trends. This challenge is particularly noticeable in regions such as the Middle East, where specific infectious diseases, such as Middle East respiratory syndrome coronavirus (MERS-CoV), have seen a dramatic increase. For such regions, it is essential to develop tools that can overcome language barriers and reach more individuals to alleviate the negative impacts of these diseases. Objective: This study aims to address these issues; therefore, we propose InfectA-Chat, a cutting-edge large language model (LLM) specifically designed for the Arabic language but also incorporating English for question and answer (Q\&A) tasks. InfectA-Chat leverages its deep understanding of the language to provide users with information on the latest trends in infectious diseases based on their queries. Methods: This comprehensive study was achieved by instruction tuning the AceGPT-7B and AceGPT-7B-Chat models on a Q\&A task, using a dataset of 55,400 Arabic and English domain--specific instruction--following data. The performance of these fine-tuned models was evaluated using 2770 domain-specific Arabic and English instruction--following data, using the GPT-4 evaluation method. A comparative analysis was then performed against Arabic LLMs and state-of-the-art models, including AceGPT-13B-Chat, Jais-13B-Chat, Gemini, GPT-3.5, and GPT-4. Furthermore, to ensure the model had access to the latest information on infectious diseases by regularly updating the data without additional fine-tuning, we used the retrieval-augmented generation (RAG) method. Results: InfectA-Chat demonstrated good performance in answering questions about infectious diseases by the GPT-4 evaluation method. Our comparative analysis revealed that it outperforms the AceGPT-7B-Chat and InfectA-Chat (based on AceGPT-7B) models by a margin of 43.52\%. It also surpassed other Arabic LLMs such as AceGPT-13B-Chat and Jais-13B-Chat by 48.61\%. Among the state-of-the-art models, InfectA-Chat achieved a leading performance of 23.78\%, competing closely with the GPT-4 model. Furthermore, the RAG method in InfectA-Chat significantly improved document retrieval accuracy. Notably, RAG retrieved more accurate documents based on queries when the top-k parameter value was increased. Conclusions: Our findings highlight the shortcomings of general Arabic LLMs in providing up-to-date information about infectious diseases. With this study, we aim to empower individuals and public health efforts by offering a bilingual Q\&A system for infectious disease monitoring. ", doi="10.2196/63881", url="https://medinform.jmir.org/2025/1/e63881" } @Article{info:doi/10.2196/66072, author="Xiong, Xin and Xiang, Linghui and Chang, Litao and Wu, XY Irene and Deng, Shuzhen", title="Forecasting the Incidence of Mumps Based on the Baidu Index and Environmental Data in Yunnan, China: Deep Learning Model Study", journal="J Med Internet Res", year="2025", month="Feb", day="6", volume="27", pages="e66072", keywords="mumps", keywords="deep learning", keywords="baidu index", keywords="forecasting", keywords="incidence prediction", keywords="time series analysis", keywords="Yunnan", keywords="China", abstract="Background: Mumps is a viral respiratory disease characterized by facial swelling and transmitted through respiratory secretions. Despite the availability of an effective vaccine, mumps outbreaks have reemerged globally, including in China, where it remains a significant public health issue. In Yunnan province, China, the incidence of mumps has fluctuated markedly and is higher than that in mainland China, underscoring the need for improved outbreak prediction methods. Traditional surveillance methods, however, may not be sufficient for timely and accurate outbreak prediction. Objective: Our study aims to leverage the Baidu search index, representing search volumes from China's most popular search engine, along with environmental data to develop a predictive model for mumps incidence in Yunnan province. Methods: We analyzed mumps incidence in Yunnan Province from 2014 to 2023, and used time series data, including mumps incidence, Baidu search index, and environmental factors, from 2016 to 2023, to develop predictive models based on long short-term memory networks. Feature selection was conducted using Pearson correlation analysis, and lag correlations were explored through a distributed nonlinear lag model (DNLM). We constructed four models with different combinations of predictors: (1) model BE, combining the Baidu index and environmental factors data; (2) model IB, combining mumps incidence and Baidu index data; (3) model IE, combining mumps incidence and environmental factors; and (4) model IBE, integrating all 3 data sources. Results: The incidence of mumps in Yunnan showed significant variability, peaking at 37.5 per 100,000 population in 2019. From 2014 to 2023, the proportion of female patients ranged from 41.3\% in 2015 to 45.7\% in 2020, consistently lower than that of male patients. After excluding variables with a Pearson correlation coefficient of <0.10 or P values of <.05, we included 3 Baidu index search term groups (disease name, symptoms, and treatment) and 6 environmental factors (maximum temperature, minimum temperature, sulfur dioxide, carbon monoxide, particulate matter with a diameter of 2.5 {\textmu}m or less, and particulate matter with a diameter of 10 {\textmu}m or less) for model development. DNLM analysis revealed that the relative risks consistently increased with rising Baidu index values, while nonlinear associations between temperature and mumps incidence were observed. Among the 4 models, model IBE exhibited the best performance, achieving the coefficient of determination of 0.72, with mean absolute error, mean absolute percentage error, and root-mean-square error values of 0.33, 15.9\%, and 0.43, respectively, in the test set. Conclusions: Our study developed model IBE to predict the incidence of mumps in Yunnan province, offering a potential tool for early detection of mumps outbreaks. The performance of model IBE underscores the potential of integrating search engine data and environmental factors to enhance mumps incidence forecasting. This approach offers a promising tool for improving public health surveillance and enabling rapid responses to mumps outbreaks. ", doi="10.2196/66072", url="https://www.jmir.org/2025/1/e66072" } @Article{info:doi/10.2196/57495, author="Kwok, Wing-Ping Nicholas and Pevnick, Joshua and Feldman, Keith", title="Elevated Ambient Temperature Associated With Reduced Infectious Disease Test Positivity Rates: Retrospective Observational Analysis of Statewide COVID-19 Testing and Weather Across California Counties", journal="JMIR Public Health Surveill", year="2024", month="Dec", day="12", volume="10", pages="e57495", keywords="body temperature", keywords="BT", keywords="fever", keywords="febrile", keywords="feverish", keywords="ambient temperature", keywords="environmental factor", keywords="environmental context", keywords="environmental", keywords="environment", keywords="COVID-19", keywords="SARS-CoV-2", keywords="coronavirus", keywords="respiratory", keywords="infectious", keywords="pulmonary", keywords="COVID-19 pandemic", keywords="pandemic", keywords="diagnostics", keywords="diagnostic test", keywords="diagnostic testing", keywords="public health surveillance", abstract="Background: From medication usage to the time of day, a number of external factors are known to alter human body temperature (BT), even in the absence of underlying pathology. In select cases, clinical guidance already suggests the consideration of clinical and demographic factors when interpreting BT, such as a decreased threshold for fever as age increases. Recent work has indicated factors impacting BT extend to environmental conditions including ambient temperature. However, the effect sizes of these relationships are often small, and it remains unclear if such relationships result in a meaningful impact on real-world health care practices. Objective: Temperature remains a common element in public health screening efforts. Leveraging the unique testing and reporting infrastructure developed around the COVID-19 pandemic, this paper uses a unique resource of daily-level statewide testing data to assess the relationship between ambient temperatures and positivity rates. As fever was a primary symptom that triggered diagnostic testing for COVID-19, this work hypothesizes that environmentally mediated BT increases would not reflect pathology, leading to decreased COVID-19 test positivity rates as temperature rises. Methods: Statewide COVID-19 polymerase chain reaction testing data curated by the California Department of Public Health were used to obtain the daily number of total tests and positivity rates for all counties across the state. These data were combined with ambient temperature data provided by the National Centers for Environmental Information for a period of 133 days between widespread testing availability and vaccine approval. A mixed-effects beta-regression model was used to estimate daily COVID-19 test positivity rate as a function of ambient temperature, population, and estimates of COVID prevalence, with nested random effects for a day of the week within unique counties across the state. Results: Considering over 19 million tests performed over 4 months and across 45 distinct counties, adjusted model results highlighted a significant negative association between daily ambient temperature and testing positivity rate (P<.001). Results of the model are strengthened as, using the same testing data, this relationship was not present in a sensitivity analysis using random daily temperatures drawn from the range of observed values (P=.52). Conclusions: These results support the underlying hypothesis and demonstrate the relationship between environmental factors and BT can impact an essential public health activity. As health care continues to operate using thresholds of BT as anchor points (ie, ?100.4 as fever) it is increasingly important to develop approaches to integrate the array of factors known to influence BT measurement. Moreover, as weather data are not often readily available in the same systems as patient data, these findings present a compelling case for future research into when and how environmental context can best be used to improve the interpretation of patient data. ", doi="10.2196/57495", url="https://publichealth.jmir.org/2024/1/e57495" } @Article{info:doi/10.2196/54597, author="Deady, Matthew and Duncan, Raymond and Sonesen, Matthew and Estiandan, Renier and Stimpert, Kelly and Cho, Sylvia and Beers, Jeffrey and Goodness, Brian and Jones, Daniel Lance and Forshee, Richard and Anderson, A. Steven and Ezzeldin, Hussein", title="A Computable Phenotype Algorithm for Postvaccination Myocarditis/Pericarditis Detection Using Real-World Data: Validation Study", journal="J Med Internet Res", year="2024", month="Nov", day="25", volume="26", pages="e54597", keywords="adverse event", keywords="vaccine safety", keywords="interoperability", keywords="computable phenotype", keywords="postmarket surveillance system", keywords="fast healthcare interoperability resources", keywords="FHIR", keywords="real-world data", keywords="validation study", keywords="Food and Drug Administration", keywords="electronic health records", keywords="COVID-19 vaccine", abstract="Background: Adverse events (AEs) associated with vaccination have traditionally been evaluated by epidemiological studies. More recently, they have gained attention due to the emergency use authorization of several COVID-19 vaccines. As part of its responsibility to conduct postmarket surveillance, the US Food and Drug Administration continues to monitor several AEs of interest to ensure the safety of vaccines, including those for COVID-19. Objective: This study is part of the Biologics Effectiveness and Safety Initiative, which aims to improve the US Food and Drug Administration's postmarket surveillance capabilities while minimizing the burden of collecting clinical data on suspected postvaccination AEs. The objective of this study was to enhance active surveillance efforts through a pilot platform that can receive automatically reported AE cases through a health care data exchange. Methods: We detected cases by sharing and applying computable phenotype algorithms to real-world data in health care providers' electronic health records databases. Using the fast healthcare interoperability resources standard for secure data transmission, we implemented a computable phenotype algorithm on a new health care system. The study focused on the algorithm's positive predictive value, validated through clinical records, assessing both the time required for implementation and the accuracy of AE detection. Results: The algorithm required 200-250 hours to implement and optimize. Of the 6,574,420 clinical encounters across 694,151 patients, 30 cases were identified as potential myocarditis/pericarditis. Of these, 26 cases were retrievable, and 24 underwent clinical validation. In total, 14 cases were confirmed as definite or probable myocarditis/pericarditis, yielding a positive predictive value of 58.3\% (95\% CI 37.3\%-76.9\%). These findings underscore the algorithm's capability for real-time detection of AEs, though they also highlight variability in performance across different health care systems. Conclusions: The study advocates for the ongoing refinement and application of distributed computable phenotype algorithms to enhance AE detection capabilities. These tools are crucial for comprehensive postmarket surveillance and improved vaccine safety monitoring. The outcomes suggest the need for further optimization to achieve more consistent results across diverse health care settings. ", doi="10.2196/54597", url="https://www.jmir.org/2024/1/e54597" } @Article{info:doi/10.2196/64969, author="Rosenthal, Sarah and Adler-Milstein, Julia and Patel, Vaishali", title="Public Health Data Exchange Through Health Information Exchange Organizations: National Survey Study", journal="JMIR Public Health Surveill", year="2024", month="Nov", day="15", volume="10", pages="e64969", keywords="public health informatics", keywords="health information exchange", keywords="health information technology", keywords="data exchange", keywords="health information", keywords="national survey", keywords="surveillance", keywords="United States", keywords="PHA", keywords="HIO", keywords="public health agency", keywords="health information exchange organization", abstract="Background: The COVID-19 pandemic revealed major gaps in public health agencies' (PHAs') data and reporting infrastructure, which limited the ability of public health officials to conduct disease surveillance, particularly among racial or ethnic minorities disproportionally affected by the pandemic. Leveraging existing health information exchange organizations (HIOs) is one possible mechanism to close these technical gaps, as HIOs facilitate health information sharing across organizational boundaries. Objective: The aim of the study is to survey all HIOs that are currently operational in the United States to assess HIO connectivity with PHAs and HIOs' capabilities to support public health data exchange. Methods: Drawing on multiple sources, we identified all potential local, regional, and state HIOs that were operational in the United States as of March 1, 2022. We defined operational as HIOs that facilitated exchange between at least 2 independent entities. We fielded a survey among our census list of 135 HIOs in January-July 2023. The survey confirmed HIO status as well as captured organizational demographics and current and potential support for PHAs. We report descriptive statistics on HIO demographics and connectivity with PHAs. We also include results on services and data available to support PHAs, funding sources to support public health reporting, and barriers to public health reporting. Of the 135 potential HIOs that received the survey, 90 met our definition of an HIO, and 77 completed the survey, yielding an 86\% response rate. Results: We found that 66 (86\%) of HIOs in 45 states were electronically connected to at least 1 PHA, yielding 187 HIO-PHA connections across all HIOs. Among HIOs connected to PHAs, the most common type of public health reporting supported by HIOs was immunization registry (n=39, 64\%), electronic laboratory result (n=37, 63\%), and syndromic surveillance (n=34, 61\%). In total, 58\% (n=38) of HIOs connected to PHAs provided data to address COVID-19 information gaps, and an additional 30\% (n=20) could do so. The most common types of data provided to PHAs were hospitalization information (n=54, 93\%), other demographic data (n=53, 91\%), health information (eg, chronic health conditions; n=51, 88\%), and hospital laboratory results (n=51, 88\%). A total of 64\% (n=42) of HIOs provided at least 1 type of data analytic service to PHAs to support COVID-19 pandemic response efforts. Top HIO reported barriers to support PHA activities included limited PHA funding (n=21, 32\%) and PHAs' competing priorities (n=15, 23\%). Conclusions: Our results show that many HIOs are already connected to PHAs and that they are assuming an emerging role to facilitate public health reporting. HIOs are well-positioned to provide value-added support for public health data exchange and address PHAs' information gaps, as ongoing federal efforts to modernize public health data infrastructure and interoperability continue. ", doi="10.2196/64969", url="https://publichealth.jmir.org/2024/1/e64969" } @Article{info:doi/10.2196/62641, author="Patel, Atushi and Maruthananth, Kevin and Matharu, Neha and Pinto, D. Andrew and Hosseini, Banafshe", title="Early Warning Systems for Acute Respiratory Infections: Scoping Review of Global Evidence", journal="JMIR Public Health Surveill", year="2024", month="Nov", day="7", volume="10", pages="e62641", keywords="early warning systems", keywords="acute respiratory infections", keywords="early detection systems", abstract="Background: Early warning systems (EWSs) are tools that integrate clinical observations to identify patterns indicating increased risks of clinical deterioration, thus facilitating timely and appropriate interventions. EWSs can mitigate the impact of global infectious diseases by enhancing information exchange, monitoring, and early detection. Objective: We aimed to evaluate the effectiveness of EWSs in acute respiratory infections (ARIs) through a scoping review of EWSs developed, described, and implemented for detecting novel, exotic, and re-emerging ARIs. Methods: We searched Ovid MEDLINE ALL, Embase, Cochrane Library (Wiley), and CINAHL (Ebsco). The search was conducted on October 03, 2023. Studies that implemented EWSs for the detection of acute respiratory illnesses were included. Covidence was used for citation management, and a modified Critical Appraisal Skills Programme (CASP) checklist was used for quality assessment. Results: From 5838 initial articles, 29 met the inclusion criteria for this review. Twelve studies evaluated the use of EWSs within community settings, ranging from rural community reporting networks to urban online participatory surveillance platforms. Five studies focused on EWSs that used data from hospitalization and emergency department visits. These systems leveraged clinical and admission data to effectively detect and manage local outbreaks of respiratory infections. Two studies focused on the effectiveness of existing surveillance systems, assessing their adaptability and responsiveness to emerging threats and how they could be improved based on past performance. Four studies highlighted the integration of machine learning models to improve the predictive accuracy of EWSs. Three studies explored the applications of national EWSs in different health care settings and emphasized their potential in predicting clinical deterioration and facilitating early intervention. Lastly, 3 studies addressed the use of surveillance systems in aged-care facilities, highlighting the unique challenges and needs of monitoring and responding to health threats in environments housing vulnerable populations. The CASP tool revealed that most studies were relevant, reliable, and of high value (score 6: 11/29, 38\%; score 5: 9/29, 31\%). The common limitations included result generalizability, selection bias, and small sample size for model validation. Conclusions: This scoping review confirms the critical role of EWSs in enhancing public health responses to respiratory infections. Although the effectiveness of these systems is evident, challenges related to generalizability and varying methodologies suggest a need for continued innovation and standardization in EWS development. ", doi="10.2196/62641", url="https://publichealth.jmir.org/2024/1/e62641" } @Article{info:doi/10.2196/55706, author="Li, Lan and Wood, E. Caroline and Kostkova, Patty", title="Evaluating a WeChat-Based Intervention to Enhance Influenza Vaccination Knowledge, Attitude, and Behavior Among Chinese University Students Residing in the United Kingdom: Controlled, Quasi-Experimental, Mixed Methods Study", journal="JMIR Form Res", year="2024", month="Oct", day="24", volume="8", pages="e55706", keywords="influenza vaccination", keywords="intervention study", keywords="social media", keywords="students", keywords="health promotion", keywords="mixed methods", abstract="Background: University students, who often live in close quarters and engage in frequent social interaction, face a heightened risk of influenza morbidity. Still, vaccination rates among this group, particularly Chinese students, remain consistently low due to limited awareness and insufficient access to vaccinations. Objective: This study examines the effectiveness of a cocreated WeChat-based intervention that targets mainland Chinese university students in the United Kingdom, aiming to improve their knowledge, attitude, and behavior (KAB) toward seasonal influenza vaccination. Methods: A quasi-experimental mixed methods design was used, incorporating an intervention and comparison group, with baseline and follow-up self-reported surveys. The study was conducted from December 19, 2022, to January 16, 2023. The primary outcome is the KAB score, which was measured before and after the intervention phases. System-recorded data and user feedback were included in the analysis as secondary outcomes. A series of hypothesis testing methods were applied to test the primary outcomes, and path analysis was used to explore the relationships. Results: Our study included 596 students, of which 303 (50.8\%) were in the intervention group and 293 (49.2\%) were in the control group. The intervention group showed significant improvements in knowledge, attitude, and intended behavior scores over time, whereas the control group had only a slight increase in intended behavior scores. When comparing changes between the 2 groups, the intervention group displayed significant differences in knowledge and attitude scores compared to the control group, while intended behavior scores did not significantly differ. After the intervention, the actual vaccination rate was slightly higher in the intervention group (63/303, 20.8\%) compared to the control group (54/293, 18.4\%). Path analysis found that the intervention had a significant direct impact on knowledge but not on attitudes; knowledge strongly influenced attitudes, and both knowledge and attitudes significantly influenced intended behavior; and there was a strong correlation between intended and actual behavior. In the intervention group, participants expressed a high level of satisfaction and positive review of the content and its use. Conclusions: This study demonstrates how a WeChat intervention effectively improves KAB related to seasonal influenza vaccination among Chinese students, highlighting the potential of social media interventions to drive vaccination behavior change. It contributes to the broader research on digital health intervention effectiveness and lays the groundwork for tailoring similar interventions to different health contexts and populations. ", doi="10.2196/55706", url="https://formative.jmir.org/2024/1/e55706" } @Article{info:doi/10.2196/47370, author="Jiang, Mingyue and Jia, Mengmeng and Wang, Qing and Sun, Yanxia and Xu, Yunshao and Dai, Peixi and Yang, Weizhong and Feng, Luzhao", title="Changes in the Epidemiological Features of Influenza After the COVID-19 Pandemic in China, the United States, and Australia: Updated Surveillance Data for Influenza Activity", journal="Interact J Med Res", year="2024", month="Oct", day="9", volume="13", pages="e47370", keywords="influenza", keywords="seasonal variation", keywords="COVID-19 pandemic", keywords="stringency index", abstract="Background: There has been a global decrease in seasonal influenza activity since the onset of the COVID-19 pandemic. Objective: We aimed to describe influenza activity during the 2021/2022 season and compare it to the trends from 2012 to 2023. We also explored the influence of social and public health prevention measures during the COVID-19 pandemic on influenza activity. Methods: We obtained influenza data from January 1, 2012, to February 5, 2023, from publicly available platforms for China, the United States, and Australia. Mitigation measures were evaluated per the stringency index, a composite index with 9 measures. A general additive model was used to assess the stringency index and the influenza positivity rate correlation, and the deviance explained was calculated. Results: We used over 200,000 influenza surveillance data. Influenza activity remained low in the United States and Australia during the 2021/2022 season. However, it increased in the United States with a positive rate of 26.2\% in the 49th week of 2022. During the 2021/2022 season, influenza activity significantly increased compared with the previous year in southern and northern China, with peak positivity rates of 28.1\% and 35.1\% in the second week of 2022, respectively. After the COVID-19 pandemic, the dominant influenza virus genotype in China was type B/Victoria, during the 2021/2022 season, and accounted for >98\% (24,541/24,908 in the South and 20,543/20,634 in the North) of all cases. Influenza virus type B/Yamagata was not detected in all these areas after the COVID-19 pandemic. Several measures individually significantly influence local influenza activity, except for influenza type B in Australia. When combined with all the measures, the deviance explained values for influenza A and B were 87.4\% (P<.05 for measures of close public transport and restrictions on international travel) and 77.6\% in southern China and 83.4\% (P<.05 for measures of school closing and close public transport) and 81.4\% in northern China, respectively. In the United States, the association was relatively stronger, with deviance-explained values of 98.6\% for influenza A and 99.1\% (P<.05 for measures of restrictions on international travel and public information campaign) for influenza B. There were no discernible effects on influenza B activity in Australia between 2020 and 2022 due to the incredibly low positive rate of influenza B. Additionally, the deviance explained values were 95.8\% (P<.05 for measures of restrictions on gathering size and restrictions on international travel) for influenza A and 72.7\% for influenza B. Conclusions: Influenza activity has increased gradually since 2021. Mitigation measures for COVID-19 showed correlations with influenza activity, mainly driven by the early stage of the pandemic. During late 2021 and 2022, the influence of mitigation management for COVID-19 seemingly decreased gradually, as the activity of influenza increased compared to the 2020/2021 season. ", doi="10.2196/47370", url="https://www.i-jmr.org/2024/1/e47370", url="http://www.ncbi.nlm.nih.gov/pubmed/39382955" } @Article{info:doi/10.2196/55208, author="Greenleaf, R. Abigail and Francis, Sarah and Zou, Jungang and Farley, M. Shannon and Lekhela, T{\vs}epang and Asiimwe, Fred and Chen, Qixuan", title="Influenza-Like Illness in Lesotho From July 2020 to July 2021: Population-Based Participatory Surveillance Results", journal="JMIR Public Health Surveill", year="2024", month="Oct", day="8", volume="10", pages="e55208", keywords="surveillance", keywords="participatory surveillance", keywords="influenza-like illness", keywords="COVID-19", keywords="cell phone", keywords="sub-Saharan Africa", keywords="population-based", keywords="Lesotho", keywords="SARS-CoV-2", keywords="technology", keywords="epidemiology", keywords="adult", keywords="data collection", keywords="innovation", keywords="mobile phone", keywords="cellphone", abstract="Background: Participatory surveillance involves at-risk populations reporting their symptoms using technology. In Lesotho, a landlocked country of 2 million people in Southern Africa, laboratory and case-based COVID-19 surveillance systems were complemented by a participatory surveillance system called ``LeCellPHIA'' (Lesotho Cell Phone Population-Based HIV Impact Assessment Survey). Objective: This report describes the person, place, and time characteristics of influenza-like illness (ILI) in Lesotho from July 15, 2020, to July 15, 2021, and reports the risk ratio of ILI by key demographic variables. Methods: LeCellPHIA employed interviewers to call participants weekly to inquire about ILI. The average weekly incidence rate for the year-long period was created using a Quasi-Poisson model, which accounted for overdispersion. To identify factors associated with an increased risk of ILI, we conducted a weekly data analysis by fitting a multilevel Poisson regression model, which accounted for 3 levels of clustering. Results: The overall response rate for the year of data collection was 75\%, which resulted in 122,985 weekly reports from 1776 participants. ILI trends from LeCellPHIA mirrored COVID-19 testing data trends, with an epidemic peak in mid to late January 2021. Overall, any ILI symptoms (eg, fever, dry cough, and shortness of breath) were reported at an average weekly rate of 879 per 100,000 (95\% CI 782?988) persons at risk. Compared to persons in the youngest age group (15?19 years), all older age groups had an elevated risk of ILI, with the highest risk of ILI in the oldest age group (?60 years; risk ratio 2.6, 95\% CI 1.7?3.8). Weekly data were shared in near real time with the National COVID-19 Secretariat and other stakeholders to monitor ILI trends, identify and respond to increases in reports of ILI, and inform policies and practices designed to reduce COVID-19 transmission in Lesotho. Conclusions: LeCellPHIA, an innovative and cost-effective system, could be replicated in countries where cell phone ownership is high but internet use is not yet high enough for a web- or app-based surveilance system. ", doi="10.2196/55208", url="https://publichealth.jmir.org/2024/1/e55208" } @Article{info:doi/10.2196/47879, author="Farooq, Kamran and Lim, Melody and Dennison-Hall, Lawrence and Janson, Finn and Olszewska, Hazel Aspen and Ahmad Zabidi, Mamduh Muhammad and Haratym-Rojek, Anna and Narowski, Karol and Clinch, Barry and Prunotto, Marco and Chawla, Devika and Hunter, Victoria and Ukachukwu, Vincent", title="Evaluation of Machine Learning to Detect Influenza Using Wearable Sensor Data and Patient-Reported Symptoms: Cohort Study", journal="J Med Internet Res", year="2024", month="Oct", day="4", volume="26", pages="e47879", keywords="influenza", keywords="influenza-like illness", keywords="wearable sensor", keywords="person-generated health care data", keywords="machine learning", abstract="Background: Machine learning offers quantitative pattern recognition analysis of wearable device data and has the potential to detect illness onset and monitor influenza-like illness (ILI) in patients who are infected. Objective: This study aims to evaluate the ability of machine-learning algorithms to distinguish between participants who are influenza positive and influenza negative in a cohort of symptomatic patients with ILI using wearable sensor (activity) data and self-reported symptom data during the latent and early symptomatic periods of ILI. Methods: This prospective observational cohort study used the extreme gradient boosting (XGBoost) classifier to determine whether a participant was influenza positive or negative based on 3 models using symptom-only data, activity-only data, and combined symptom and activity data. Data were collected from the Home Testing of Respiratory Illness (HTRI) study and FluStudy2020, both conducted between December 2019 and October 2020. The model was developed using the FluStudy2020 data and tested on the HTRI data. Analyses included participants in these studies with an at-home influenza diagnostic test result. Fitbit (Google LLC) devices were used to measure participants' steps, heart rate, and sleep parameters. Participants detailed their ILI symptoms, health care--seeking behaviors, and quality of life. Model performance was assessed by area under the curve (AUC), balanced accuracy, recall (sensitivity), specificity, precision (positive predictive value), negative predictive value, and weighted harmonic mean of precision and recall (F2) score. Results: An influenza diagnostic test result was available for 953 and 925 participants in HTRI and FluStudy2020, respectively, of whom 848 (89\%) and 840 (90.8\%) had activity data. For the training and validation sets, the highest performing model was trained on the combined symptom and activity data (training AUC=0.77; validation AUC=0.74) versus symptom-only (training AUC=0.73; validation AUC=0.72) and activity-only (training AUC=0.68; validation AUC=0.65) data. For the FluStudy2020 test set, the performance of the model trained on combined symptom and activity data was closely aligned with that of the symptom-only model (combined symptom and activity test AUC=0.74; symptom-only test AUC=0.74). These results were validated using independent HTRI data (combined symptom and activity evaluation AUC=0.75; symptom-only evaluation AUC=0.74). The top features guiding influenza detection were cough; mean resting heart rate during main sleep; fever; total minutes in bed for the combined model; and fever, cough, and sore throat for the symptom-only model. Conclusions: Machine-learning algorithms had moderate accuracy in detecting influenza, suggesting that previous findings from research-grade sensors tested in highly controlled experimental settings may not easily translate to scalable commercial-grade sensors. In the future, more advanced wearable sensors may improve their performance in the early detection and discrimination of viral respiratory infections. ", doi="10.2196/47879", url="https://www.jmir.org/2024/1/e47879", url="http://www.ncbi.nlm.nih.gov/pubmed/39365646" } @Article{info:doi/10.2196/60319, author="Gribbin, William and Dejonge, Peter and Rodseth, Jakob and Hashikawa, Andrew", title="Advancing Public Health Surveillance in Child Care Centers: Stakeholder-Informed Redesign and User Satisfaction Evaluation of the MCRISP Network", journal="JMIR Public Health Surveill", year="2024", month="Sep", day="24", volume="10", pages="e60319", keywords="public health", keywords="disease surveillance", keywords="data collection", keywords="dashboard", keywords="child care", keywords="child", keywords="children", keywords="care center", keywords="user satisfaction", keywords="ill", keywords="illness", keywords="transmission", keywords="tracking", keywords="tracker", keywords="COVID-19", keywords="SARS-CoV-2", keywords="pandemic", keywords="disease monitoring", keywords="technology", keywords="respiratory", keywords="gastrointestinal", keywords="user-centered design", keywords="infectious disease", keywords="visualization", doi="10.2196/60319", url="https://publichealth.jmir.org/2024/1/e60319" } @Article{info:doi/10.2196/54861, author="Lin, Ting-Yu and Yen, Ming-Fang Amy and Chen, Li-Sheng Sam and Hsu, Chen-Yang and Lai, Chao-Chih and Luh, Dih-Ling and Yeh, Yen-Po and Chen, Hsiu-Hsi Tony", title="Kinetics of Viral Shedding for Outbreak Surveillance of Emerging Infectious Diseases: Modeling Approach to SARS-CoV-2 Alpha and Omicron Infection", journal="JMIR Public Health Surveill", year="2024", month="Sep", day="19", volume="10", pages="e54861", keywords="COVID-19", keywords="PCR testing", keywords="Ct values", keywords="viral load", keywords="kinetics of viral shedding", keywords="emerging infectious disease", keywords="SARS-CoV-2 variants", keywords="infection surveillance", abstract="Background: Previous studies have highlighted the importance of viral shedding using cycle threshold (Ct) values obtained via reverse transcription polymerase chain reaction to understand the epidemic trajectories of SARS-CoV-2 infections. However, it is rare to elucidate the transition kinetics of Ct values from the asymptomatic or presymptomatic phase to the symptomatic phase before recovery using individual repeated Ct values. Objective: This study proposes a novel Ct-enshrined compartment model to provide a series of quantitative measures for delineating the full trajectories of the dynamics of viral load from infection until recovery. Methods: This Ct-enshrined compartment model was constructed by leveraging Ct-classified states within and between presymptomatic and symptomatic compartments before recovery or death among people with infections. A series of recovery indices were developed to assess the net kinetic movement of Ct-up toward and Ct-down off recovery. The model was applied to (1) a small-scale community-acquired Alpha variant outbreak under the ``zero-COVID-19'' policy without vaccines in May 2021 and (2) a large-scale community-acquired Omicron variant outbreak with high booster vaccination rates following the lifting of the ``zero-COVID-19'' policy in April 2022 in Taiwan. The model used Bayesian Markov chain Monte Carlo methods with the Metropolis-Hastings algorithm for parameter estimation. Sensitivity analyses were conducted by varying Ct cutoff values to assess the robustness of the model. Results: The kinetic indicators revealed a marked difference in viral shedding dynamics between the Alpha and Omicron variants. The Alpha variant exhibited slower viral shedding and lower recovery rates, but the Omicron variant demonstrated swifter viral shedding and higher recovery rates. Specifically, the Alpha variant showed gradual Ct-up transitions and moderate recovery rates, yielding a presymptomatic recovery index slightly higher than 1 (1.10), whereas the Omicron variant had remarkable Ct-up transitions and significantly higher asymptomatic recovery rates, resulting in a presymptomatic recovery index much higher than 1 (152.5). Sensitivity analysis confirmed the robustness of the chosen Ct values of 18 and 25 across different recovery phases. Regarding the impact of vaccination, individuals without booster vaccination had a 19\% higher presymptomatic incidence rate compared to those with booster vaccination. Breakthrough infections in boosted individuals initially showed similar Ct-up transition rates but higher rates in later stages compared to nonboosted individuals. Overall, booster vaccination improved recovery rates, particularly during the symptomatic phase, although recovery rates for persistent asymptomatic infection were similar regardless of vaccination status once the Ct level exceeded 25. Conclusions: The study provides new insights into dynamic Ct transitions, with the notable finding that Ct-up transitions toward recovery outpaced Ct-down and symptom-surfacing transitions during the presymptomatic phase. The Ct-up against Ct-down transition varies with variants and vaccination status. The proposed Ct-enshrined compartment model is useful for the surveillance of emerging infectious diseases in the future to prevent community-acquired outbreaks. ", doi="10.2196/54861", url="https://publichealth.jmir.org/2024/1/e54861" } @Article{info:doi/10.2196/58704, author="Elliot, J. Alex and Hughes, E. Helen and Harcourt, E. Sally and Smith, Sue and Loveridge, Paul and Morbey, A. Roger and Bains, Amardeep and Edeghere, Obaghe and Jones, R. Natalia and Todkill, Daniel and Smith, E. Gillian", title="From Fax to Secure File Transfer Protocol: The 25-Year Evolution of Real-Time Syndromic Surveillance in England", journal="J Med Internet Res", year="2024", month="Sep", day="17", volume="26", pages="e58704", keywords="epidemiology", keywords="population surveillance", keywords="sentinel surveillance", keywords="public health surveillance", keywords="bioterrorism", keywords="mass gathering", keywords="pandemics", doi="10.2196/58704", url="https://www.jmir.org/2024/1/e58704" } @Article{info:doi/10.2196/55613, author="Lopes, Henrique and Baptista-Leite, Ricardo and Hermenegildo, Catarina and Atun, Rifat", title="Digital Gamification Tool (Let's Control Flu) to Increase Vaccination Coverage Rates: Proposal for Algorithm Development", journal="JMIR Res Protoc", year="2024", month="Sep", day="10", volume="13", pages="e55613", keywords="influenza", keywords="gamification", keywords="public health policies", keywords="vaccination coverage rates", keywords="health promotion", abstract="Background: Influenza represents a critical public health challenge, disproportionately affecting at-risk populations, including older adults and those with chronic conditions, often compounded by socioeconomic factors. Innovative strategies, such as gamification, are essential for augmenting risk communication and community engagement efforts to address this threat. Objective: This study aims to introduce the ``Let's Control Flu'' (LCF) tool, a gamified, interactive platform aimed at simulating the impact of various public health policies (PHPs) on influenza vaccination coverage rates and health outcomes. The tool aligns with the World Health Organization's goal of achieving a 75\% influenza vaccination rate by 2030, facilitating strategic decision-making to enhance vaccination uptake. Methods: The LCF tool integrates a selection of 13 PHPs from an initial set proposed in another study, targeting specific population groups to evaluate 7 key health outcomes. A prioritization mechanism accounts for societal resistance and the synergistic effects of PHPs, projecting the potential policy impacts from 2022 to 2031. This methodology enables users to assess how PHPs could influence public health strategies within distinct target groups. Results: The LCF project began in February 2021 and is scheduled to end in December 2024. The model creation phase and its application to the pilot country, Sweden, took place between May 2021 and May 2023, with subsequent application to other European countries. The pilot phase demonstrated the tool's potential, indicating a promising increase in the national influenza vaccination coverage rate, with uniform improvements across all targeted demographic groups. These initial findings highlight the tool's capacity to model the effects of PHPs on improving vaccination rates and mitigating the health impact of influenza. Conclusions: By incorporating gamification into the analysis of PHPs, the LCF tool offers an innovative and accessible approach to supporting health decision makers and patient advocacy groups. It enhances the comprehension of policy impacts, promoting more effective influenza prevention and control strategies. This paper underscores the critical need for adaptable and engaging tools in PHP planning and implementation. International Registered Report Identifier (IRRID): RR1-10.2196/55613 ", doi="10.2196/55613", url="https://www.researchprotocols.org/2024/1/e55613", url="http://www.ncbi.nlm.nih.gov/pubmed/39255031" } @Article{info:doi/10.2196/45513, author="Harrigan, P. Sean and Vel{\'a}squez Garc{\'i}a, A. H{\'e}ctor and Abdia, Younathan and Wilton, James and Prystajecky, Natalie and Tyson, John and Fjell, Chris and Hoang, Linda and Kwong, C. Jeffrey and Mishra, Sharmistha and Wang, Linwei and Sander, Beate and Janjua, Z. Naveed and Sbihi, Hind", title="The Clinical Severity of COVID-19 Variants of Concern: Retrospective Population-Based Analysis", journal="JMIR Public Health Surveill", year="2024", month="Aug", day="27", volume="10", pages="e45513", keywords="COVID-19", keywords="SARS-CoV-2", keywords="severity", keywords="whole genome sequencing", keywords="WGS", keywords="social determinants of health", keywords="SDOHs", keywords="vaccination", keywords="variants of concern", keywords="VOCs", keywords="Alpha", keywords="Gamma", keywords="Delta", keywords="Omicron", abstract="Background: SARS-CoV-2 variants of concern (VOCs) emerged and rapidly replaced the original strain worldwide. The increased transmissibility of these new variants led to increases in infections, hospitalizations, and mortality. However, there is a scarcity of retrospective investigations examining the severity of all the main VOCs in presence of key public health measures and within various social determinants of health (SDOHs). Objective: This study aims to provide a retrospective assessment of the clinical severity of COVID-19 VOCs in the context of heterogenous SDOHs and vaccination rollout. Methods: We used a population-based retrospective cohort design with data from the British Columbia COVID-19 Cohort, a linked provincial surveillance platform. To assess the relative severity (hospitalizations, intensive care unit [ICU] admissions, and deaths) of Gamma, Delta, and Omicron infections during 2021 relative to Alpha, we used inverse probability treatment weighted Cox proportional hazard modeling. We also conducted a subanalysis among unvaccinated individuals, as assessed severity differed across VOCs and SDOHs. Results: We included 91,964 individuals infected with a SARS-CoV-2 VOC (Alpha: n=20,487, 22.28\%; Gamma: n=15,223, 16.55\%; Delta: n=49,161, 53.46\%; and Omicron: n=7093, 7.71\%). Delta was associated with the most severe disease in terms of hospitalization, ICU admissions, and deaths (hospitalization: adjusted hazard ratio [aHR] 2.00, 95\% CI 1.92-2.08; ICU: aHR 2.05, 95\% CI 1.91-2.20; death: aHR 3.70, 95\% CI 3.23-4.25 relative to Alpha), followed generally by Gamma and then Omicron and Alpha. The relative severity by VOC remained similar in the unvaccinated individual subanalysis, although the proportion of individuals infected with Delta and Omicron who were hospitalized was 2 times higher in those unvaccinated than in those fully vaccinated. Regarding SDOHs, the proportion of hospitalized individuals was higher in areas with lower income across all VOCs, whereas among Alpha and Gamma infections, 2 VOCs that cocirculated, differential distributions of hospitalizations were found among racially minoritized groups. Conclusions: Our study provides robust severity estimates for all VOCs during the COVID-19 pandemic in British Columbia, Canada. Relative to Alpha, we found Delta to be the most severe, followed by Gamma and Omicron. This study highlights the importance of targeted testing and sequencing to ensure timely detection and accurate estimation of severity in emerging variants. It further sheds light on the importance of vaccination coverage and SDOHs in the context of pandemic preparedness to support the prioritization of allocation for resource-constrained or minoritized groups. ", doi="10.2196/45513", url="https://publichealth.jmir.org/2024/1/e45513", url="http://www.ncbi.nlm.nih.gov/pubmed/39190434" } @Article{info:doi/10.2196/43173, author="Alsallakh, Mohammad and Adeloye, Davies and Vasileiou, Eleftheria and Sivakumaran, Shanya and Akbari, Ashley and Lyons, A. Ronan and Robertson, Chris and Rudan, Igor and Davies, A. Gwyneth and Sheikh, Aziz", title="Impact of the COVID-19 Pandemic on Influenza Hospital Admissions and Deaths in Wales: Descriptive National Time Series Analysis", journal="JMIR Public Health Surveill", year="2024", month="Aug", day="21", volume="10", pages="e43173", keywords="influenza", keywords="hospitalization", keywords="mortality", keywords="COVID-19 pandemic", keywords="nonpharmaceutical interventions", keywords="Wales", keywords="COVID-19", keywords="community health", keywords="hospital admission", keywords="endemic virus", keywords="public health surveillance", abstract="Background: The COVID-19 pandemic and the ensuing implementation of control measures caused widespread societal disruption. These disruptions may also have affected community transmission and seasonal circulation patterns of endemic respiratory viruses. Objective: We aimed to investigate the impact of COVID-19--related disruption on influenza-related emergency hospital admissions and deaths in Wales in the first 2 years of the pandemic. Methods: A descriptive analysis of influenza activity was conducted using anonymized pathology, hospitalization, and mortality data from the Secure Anonymised Information Linkage Databank in Wales. The annual incidence of emergency hospitalizations and deaths with influenza-specific diagnosis codes between January 1, 2015, and December 31, 2021, was estimated. Case definitions of emergency hospitalization and death required laboratory confirmation with a polymerase chain reaction test. Trends of admissions and deaths were analyzed monthly and yearly. We conducted 2 sensitivity analyses by extending case definitions to include acute respiratory illnesses with a positive influenza test and by limiting admissions to those with influenza as the primary diagnosis. We also examined yearly influenza testing trends to understand changes in testing behavior during the pandemic. Results: We studied a population of 3,235,883 Welsh residents in 2020 with a median age of 42.5 (IQR 22.9--61.0) years. Influenza testing in Wales increased notably in the last 2 months of 2020, and particularly in 2021 to 39,720 per 100,000 people, compared to the prepandemic levels (1343 in 2019). The percentage of influenza admissions matched to an influenza polymerase chain reaction test increased from 74.8\% (1890/2526) in 2019 to 85.2\% (98/115) in 2021. However, admissions with a positive test per 100,000 population decreased from 17.0 in 2019 to 2.7 and 0.6 in 2020 and 2021, respectively. Similarly, deaths due to influenza with a positive influenza test per 100,000 population decreased from 0.4 in 2019 to 0.0 in 2020 and 2021. Sensitivity analyses showed similar patterns of decreasing influenza admissions and deaths in the first 2 years of the COVID-19 pandemic. Conclusions: Nonpharmaceutical interventions to control COVID-19 were associated with a substantial reduction in the transmission of the influenza virus, with associated substantial reductions in hospital cases and deaths observed. Beyond the pandemic context, consideration should be given to the role of nonpharmaceutical community-driven interventions to reduce the burden of influenza. ", doi="10.2196/43173", url="https://publichealth.jmir.org/2024/1/e43173" } @Article{info:doi/10.2196/55822, author="Wang, Haitao and Geng, Mengjie and Schikowski, Tamara and Areal, Tracey Ashtyn and Hu, Kejia and Li, Wen and Coelho, Stagliorio Micheline de Sousa Zanotti and Saldiva, Nascimento Paulo Hil{\'a}rio and Sun, Wei and Zhou, Chengchao and Lu, Liang and Zhao, Qi and Ma, Wei", title="Increased Risk of Influenza Infection During Cold Spells in China: National Time Series Study", journal="JMIR Public Health Surveill", year="2024", month="Aug", day="13", volume="10", pages="e55822", keywords="influenza", keywords="cold spell", keywords="definition", keywords="vulnerable population", keywords="modification effect", keywords="China", abstract="Background: Studies have reported the adverse effects of cold events on influenza. However, the role of critical factors, such as characteristics of cold spells, and regional variations remain unresolved. Objective: We aimed to systematically evaluate the association between cold spells and influenza incidence in mainland China. Methods: This time series analysis used surveillance data of daily influenza from 325 sites in China in the 2014?2019 period. A total of 15 definitions of cold spells were adopted based on combinations of temperature thresholds and days of duration. A distributed lag linear model was used to estimate the short-term effects of cold spells on influenza incidence during the cool seasons (November to March), and we further explored the potential impact of cold spell characteristics (ie, intensity, duration, and timing during the season) on the estimated associations. Meta-regressions were used to evaluate the modification effect of city-level socioeconomic indicators. Results: The overall effect of cold spells on influenza incidence increased with the temperature threshold used to define cold spells, whereas the added effects were generally small and not statistically significant. The relative risk of influenza-associated with cold spells was 3.35 (95\% CI 2.89?3.88), and the estimated effects were stronger during the middle period of cool seasons. The health effects of cold spells varied geographically and residents in Jiangnan region were vulnerable groups (relative risk 7.36, 95\% CI 5.44?9.95). The overall effects of cold spells were positively correlated with the urban population density, population size, gross domestic product per capita, and urbanization rate, indicating a sterner response to cold spells in metropolises. Conclusions: Cold spells create a substantial health burden on seasonal influenza in China. Findings on regional and socioeconomic differences in the health effects of cold spells on seasonal influenza may be useful in formulating region-specific public health policies to address the hazardous effects of cold spells. ", doi="10.2196/55822", url="https://publichealth.jmir.org/2024/1/e55822" } @Article{info:doi/10.2196/57349, author="Aronis, M. John and Ye, Ye and Espino, Jessi and Hochheiser, Harry and Michaels, G. Marian and Cooper, F. Gregory", title="A Bayesian System to Detect and Track Outbreaks of Influenza-Like Illnesses Including Novel Diseases: Algorithm Development and Validation", journal="JMIR Public Health Surveill", year="2024", month="Aug", day="13", volume="10", pages="e57349", keywords="biosurveillance", keywords="outbreak", keywords="novel disease", keywords="natural language processing", keywords="disease modeling", keywords="Bayesian modeling", keywords="influenza", keywords="influenza-like illnesses", keywords="novel diseases", keywords="public health", keywords="COVID-19", keywords="SARS-CoV-2", keywords="coronavirus", keywords="hospital", keywords="hospitals", keywords="emergency department", keywords="patient care", keywords="NLP", keywords="algorithm", keywords="respiratory syncytial", keywords="human metapneumovirus", keywords="parainfluenza", keywords="Pennsylvania", keywords="enterovirus D68", keywords="surveillance", abstract="Background: ?The early identification of outbreaks of both known and novel influenza-like illnesses (ILIs) is an important public health problem. Objective: ?This study aimed to describe the design and testing of a tool that detects and tracks outbreaks of both known and novel ILIs, such as the SARS-CoV-2 worldwide pandemic, accurately and early. Methods: ?This paper describes the ILI Tracker algorithm that first models the daily occurrence of a set of known ILIs in hospital emergency departments in a monitored region using findings extracted from patient care reports using natural language processing. We then show how the algorithm can be extended to detect and track the presence of an unmodeled disease that may represent a novel disease outbreak. Results: ?We include results based on modeling diseases like influenza, respiratory syncytial virus, human metapneumovirus, and parainfluenza for 5 emergency departments in Allegheny County, Pennsylvania, from June 1, 2014, to May 31, 2015. We also include the results of detecting the outbreak of an unmodeled disease, which in retrospect was very likely an outbreak of the enterovirus D68 (EV-D68). Conclusions: ?The results reported in this paper provide support that ILI Tracker was able to track well the incidence of 4 modeled influenza-like diseases over a 1-year period, relative to laboratory-confirmed cases, and it was computationally efficient in doing so. The system was also able to detect a likely novel outbreak of EV-D68 early in an outbreak that occurred in Allegheny County in 2014 as well as clinically characterize that outbreak disease accurately. ", doi="10.2196/57349", url="https://publichealth.jmir.org/2024/1/e57349", url="http://www.ncbi.nlm.nih.gov/pubmed/38805611" } @Article{info:doi/10.2196/37625, author="Hsiao, Hsun Kai and Quinn, Emma and Johnstone, Travers and Gomez, Maria and Ingleton, Andrew and Parasuraman, Arun and Najjar, Zeina and Gupta, Leena", title="A Novel Web-Based Application for Influenza and COVID-19 Outbreak Detection and Response in Residential Aged Care Facilities", journal="JMIR Public Health Surveill", year="2024", month="Jun", day="24", volume="10", pages="e37625", keywords="web application", keywords="digital health", keywords="communicable disease control", keywords="outbreak", keywords="surveillance", keywords="influenza", keywords="aged care", keywords="aged care homes", doi="10.2196/37625", url="https://publichealth.jmir.org/2024/1/e37625" } @Article{info:doi/10.2196/56064, author="Wang, Qiang and Yang, Liuqing and Xiu, Shixin and Shen, Yuan and Jin, Hui and Lin, Leesa", title="A Prediction Model for Identifying Seasonal Influenza Vaccination Uptake Among Children in Wuxi, China: Prospective Observational Study", journal="JMIR Public Health Surveill", year="2024", month="Jun", day="17", volume="10", pages="e56064", keywords="influenza", keywords="vaccination", keywords="children", keywords="prediction model", keywords="China", keywords="vaccine", keywords="behaviors", keywords="health care professional", keywords="intervention", keywords="sociodemographics", keywords="vaccine hesitancy", keywords="clinic", keywords="Bayesian network", keywords="logistic regression", keywords="accuracy", keywords="Cohen $\kappa$", keywords="prediction", keywords="public health", keywords="immunization", keywords="digital age", abstract="Background: Predicting vaccination behaviors accurately could provide insights for health care professionals to develop targeted interventions. Objective: The aim of this study was to develop predictive models for influenza vaccination behavior among children in China. Methods: We obtained data from a prospective observational study in Wuxi, eastern China. The predicted outcome was individual-level vaccine uptake and covariates included sociodemographics of the child and parent, parental vaccine hesitancy, perceptions of convenience to the clinic, satisfaction with clinic services, and willingness to vaccinate. Bayesian networks, logistic regression, least absolute shrinkage and selection operator (LASSO) regression, support vector machine (SVM), naive Bayes (NB), random forest (RF), and decision tree classifiers were used to construct prediction models. Various performance metrics, including area under the receiver operating characteristic curve (AUC), were used to evaluate the predictive performance of the different models. Receiver operating characteristic curves and calibration plots were used to assess model performance. Results: A total of 2383 participants were included in the study; 83.2\% of these children (n=1982) were <5 years old and 6.6\% (n=158) had previously received an influenza vaccine. More than half (1356/2383, 56.9\%) the parents indicated a willingness to vaccinate their child against influenza. Among the 2383 children, 26.3\% (n=627) received influenza vaccination during the 2020-2021 season. Within the training set, the RF model showed the best performance across all metrics. In the validation set, the logistic regression model and NB model had the highest AUC values; the SVM model had the highest precision; the NB model had the highest recall; and the logistic regression model had the highest accuracy, F1 score, and Cohen $\kappa$ value. The LASSO and logistic regression models were well-calibrated. Conclusions: The developed prediction model can be used to quantify the uptake of seasonal influenza vaccination for children in China. The stepwise logistic regression model may be better suited for prediction purposes. ", doi="10.2196/56064", url="https://publichealth.jmir.org/2024/1/e56064", url="http://www.ncbi.nlm.nih.gov/pubmed/38885032" } @Article{info:doi/10.2196/56271, author="Leston, Meredith and Ord{\'o}{\~n}ez-Mena, Jos{\'e} and Joy, Mark and de Lusignan, Simon and Hobbs, Richard and McInnes, Iain and Lee, Lennard", title="Defining and Risk-Stratifying Immunosuppression (the DESTINIES Study): Protocol for an Electronic Delphi Study", journal="JMIR Res Protoc", year="2024", month="Jun", day="6", volume="13", pages="e56271", keywords="immunosuppressed", keywords="immunocompromised", keywords="COVID", keywords="vaccines", keywords="COVID-19", keywords="surveillance", keywords="phenotype", keywords="adult", keywords="immunosuppression", keywords="clinical risk", keywords="disease surveillance", keywords="clinical consensus", keywords="eDelphi", keywords="immunosuppressed patient", keywords="immunosuppressed patients", keywords="study design", keywords="Delphi", keywords="methods", keywords="methodology", keywords="statistic", keywords="statistics", keywords="statistical", keywords="consensus", keywords="immune", keywords="immunity", keywords="immunology", keywords="immunological", abstract="Background: Globally, there are marked inconsistencies in how immunosuppression is characterized and subdivided into clinical risk groups. This is detrimental to the precision and comparability of disease surveillance efforts---which has negative implications for the care of those who are immunosuppressed and their health outcomes. This was particularly apparent during the COVID-19 pandemic; despite collective motivation to protect these patients, conflicting clinical definitions created international rifts in how those who were immunosuppressed were monitored and managed during this period. We propose that international clinical consensus be built around the conditions that lead to immunosuppression and their gradations of severity concerning COVID-19. Such information can then be formalized into a digital phenotype to enhance disease surveillance and provide much-needed intelligence on risk-prioritizing these patients. Objective: We aim to demonstrate how electronic Delphi objectives, methodology, and statistical approaches will help address this lack of consensus internationally and deliver a COVID-19 risk-stratified phenotype for ``adult immunosuppression.'' Methods: Leveraging existing evidence for heterogeneous COVID-19 outcomes in adults who are immunosuppressed, this work will recruit over 50 world-leading clinical, research, or policy experts in the area of immunology or clinical risk prioritization. After 2 rounds of clinical consensus building and 1 round of concluding debate, these panelists will confirm the medical conditions that should be classed as immunosuppressed and their differential vulnerability to COVID-19. Consensus statements on the time and dose dependencies of these risks will also be presented. This work will be conducted iteratively, with opportunities for panelists to ask clarifying questions between rounds and provide ongoing feedback to improve questionnaire items. Statistical analysis will focus on levels of agreement between responses. Results: This protocol outlines a robust method for improving consensus on the definition and meaningful subdivision of adult immunosuppression concerning COVID-19. Panelist recruitment took place between April and May of 2024; the target set for over 50 panelists was achieved. The study launched at the end of May and data collection is projected to end in July 2024. Conclusions: This protocol, if fully implemented, will deliver a universally acceptable, clinically relevant, and electronic health record--compatible phenotype for adult immunosuppression. As well as having immediate value for COVID-19 resource prioritization, this exercise and its output hold prospective value for clinical decision-making across all diseases that disproportionately affect those who are immunosuppressed. International Registered Report Identifier (IRRID): PRR1-10.2196/56271 ", doi="10.2196/56271", url="https://www.researchprotocols.org/2024/1/e56271", url="http://www.ncbi.nlm.nih.gov/pubmed/38842925" } @Article{info:doi/10.2196/39297, author="Hoang, Uy and Delanerolle, Gayathri and Fan, Xuejuan and Aspden, Carole and Byford, Rachel and Ashraf, Mansoor and Haag, Mendel and Elson, William and Leston, Meredith and Anand, Sneha and Ferreira, Filipa and Joy, Mark and Hobbs, Richard and de Lusignan, Simon", title="A Profile of Influenza Vaccine Coverage for 2019-2020: Database Study of the English Primary Care Sentinel Cohort", journal="JMIR Public Health Surveill", year="2024", month="May", day="24", volume="10", pages="e39297", keywords="medical records systems", keywords="computerize", keywords="influenza", keywords="influenza vaccines", keywords="sentinel surveillance", keywords="vocabulary controlled", keywords="general practitioners", keywords="general practice", keywords="primary health care", keywords="vaccine", keywords="public health", keywords="surveillance", keywords="uptake", abstract="Background: Innovation in seasonal influenza vaccine development has resulted in a wider range of formulations becoming available. Understanding vaccine coverage across populations including the timing of administration is important when evaluating vaccine benefits and risks. Objective: This study aims to report the representativeness, uptake of influenza vaccines, different formulations of influenza vaccines, and timing of administration within the English Primary Care Sentinel Cohort (PCSC). Methods: We used the PCSC of the Oxford-Royal College of General Practitioners Research and Surveillance Centre. We included patients of all ages registered with PCSC member general practices, reporting influenza vaccine coverage between September 1, 2019, and January 29, 2020. We identified influenza vaccination recipients and characterized them by age, clinical risk groups, and vaccine type. We reported the date of influenza vaccination within the PCSC by International Standard Organization (ISO) week. The representativeness of the PCSC population was compared with population data provided by the Office for National Statistics. PCSC influenza vaccine coverage was compared with published UK Health Security Agency's national data. We used paired t tests to compare populations, reported with 95\% CI. Results: The PCSC comprised 7,010,627 people from 693 general practices. The study population included a greater proportion of people aged 18-49 years (2,982,390/7,010,627, 42.5\%; 95\% CI 42.5\%-42.6\%) compared with the Office for National Statistics 2019 midyear population estimates (23,219,730/56,286,961, 41.3\%; 95\% CI 4.12\%-41.3\%; P<.001). People who are more deprived were underrepresented and those in the least deprived quintile were overrepresented. Within the study population, 24.7\% (1,731,062/7,010,627; 95\% CI 24.7\%-24.7\%) of people of all ages received an influenza vaccine compared with 24.2\% (14,468,665/59,764,928; 95\% CI 24.2\%-24.2\%; P<.001) in national data. The highest coverage was in people aged ?65 years (913,695/1,264,700, 72.3\%; 95\% CI 72.2\%-72.3\%). The proportion of people in risk groups who received an influenza vaccine was also higher; for example, 69.8\% (284,280/407,228; 95\% CI 69.7\%-70\%) of people with diabetes in the PCSC received an influenza vaccine compared with 61.2\% (983,727/1,607,996; 95\% CI 61.1\%-61.3\%; P<.001) in national data. In the PCSC, vaccine type and brand information were available for 71.8\% (358,365/498,923; 95\% CI 71.7\%-72\%) of people aged 16-64 years and 81.9\% (748,312/913,695; 95\% CI 81.8\%-82\%) of people aged ?65 years, compared with 23.6\% (696,880/2,900,000) and 17.8\% (1,385,888/7,700,000), respectively, of the same age groups in national data. Vaccination commenced during ISO week 35, continued until ISO week 3, and peaked during ISO week 41. The in-week peak in vaccination administration was on Saturdays. Conclusions: The PCSC's sociodemographic profile was similar to the national population and captured more data about risk groups, vaccine brands, and batches. This may reflect higher data quality. Its capabilities included reporting precise dates of administration. The PCSC is suitable for undertaking studies of influenza vaccine coverage. ", doi="10.2196/39297", url="https://publichealth.jmir.org/2024/1/e39297", url="http://www.ncbi.nlm.nih.gov/pubmed/38787605" } @Article{info:doi/10.2196/47626, author="Chen, Li and Wang, Liping and Xing, Yi and Xie, Junqing and Su, Binbin and Geng, Mengjie and Ren, Xiang and Zhang, Yi and Liu, Jieyu and Ma, Tao and Chen, Manman and Miller, E. Jessica and Dong, Yanhui and Song, Yi and Ma, Jun and Sawyer, Susan", title="Persistence and Variation of the Indirect Effects of COVID-19 Restrictions on the Spectrum of Notifiable Infectious Diseases in China: Analysis of National Surveillance Among Children and Adolescents From 2018 to 2021", journal="JMIR Public Health Surveill", year="2024", month="May", day="15", volume="10", pages="e47626", keywords="children and adolescents", keywords="COVID-19", keywords="notifiable infectious diseases", abstract="Background: Beyond the direct effect of COVID-19 infection on young people, the wider impact of the pandemic on other infectious diseases remains unknown. Objective: This study aims to assess changes in the incidence and mortality of 42 notifiable infectious diseases during the pandemic among children and adolescents in China, compared with prepandemic levels. Methods: The Notifiable Infectious Disease Surveillance System of China was used to detect new cases and fatalities among individuals aged 5-22 years across 42 notifiable infectious diseases spanning from 2018 to 2021. These infectious diseases were categorized into 5 groups: respiratory, gastrointestinal and enterovirus, sexually transmitted and blood-borne, zoonotic, and vector-borne diseases. Each year (2018-2021) was segmented into 4 phases: phase 1 (January 1-22), phase 2 (January 23-April 7), phase 3 (April 8-August 31), and phase 4 (September 1-December 31) according to the varying intensities of pandemic restrictive measures in 2020. Generalized linear models were applied to assess the change in the incidence and mortality within each disease category, using 2018 and 2019 as the reference. Results: A total of 4,898,260 incident cases and 3701 deaths were included. The overall incidence of notifiable infectious diseases decreased sharply during the first year of the COVID-19 pandemic (2020) compared with prepandemic levels (2018 and 2019), and then rebounded in 2021, particularly in South China. Across the past 4 years, the number of deaths steadily decreased. The incidence of diseases rebounded differentially by the pandemic phase. For instance, although seasonal influenza dominated respiratory diseases in 2019, it showed a substantial decline during the pandemic (percent change in phase 2 2020: 0.21, 95\% CI 0.09-0.50), which persisted until 2021 (percent change in phase 4 2021: 1.02, 95\% CI 0.74-1.41). The incidence of gastrointestinal and enterovirus diseases decreased by 33.6\% during 2020 but rebounded by 56.9\% in 2021, mainly driven by hand, foot, and mouth disease (percent change in phase 3 2021: 1.28, 95\% CI 1.17-1.41) and infectious diarrhea (percent change in phase 3 2020: 1.22, 95\% CI 1.17-1.28). Sexually transmitted and blood-borne diseases were restrained during the first year of 2021 but rebounded quickly in 2021, mainly driven by syphilis (percent change in phase 3 2020: 1.31, 95\% CI 1.23-1.40) and gonorrhea (percent change in phase 3 2020: 1.10, 95\% CI 1.05-1.16). Zoonotic diseases were not dampened by the pandemic but continued to increase across the study period, mainly due to brucellosis (percent change in phase 2 2020: 0.94, 95\% CI 0.75-1.16). Vector-borne diseases showed a continuous decline during 2020, dominated by hemorrhagic fever (percent change in phase 2 2020: 0.68, 95\% CI 0.53-0.87), but rebounded in 2021. Conclusions: The COVID-19 pandemic was associated with a marked decline in notifiable infectious diseases in Chinese children and adolescents. These effects were not sustained, with evidence of a rebound to prepandemic levels by late 2021. To effectively address the postpandemic resurgence of infectious diseases in children and adolescents, it will be essential to maintain disease surveillance and strengthen the implementation of various initiatives. These include extending immunization programs, prioritizing the management of sexually transmitted infections, continuing feasible nonpharmaceutical intervention projects, and effectively managing imported infections. ", doi="10.2196/47626", url="https://publichealth.jmir.org/2024/1/e47626", url="http://www.ncbi.nlm.nih.gov/pubmed/38748469" } @Article{info:doi/10.2196/40792, author="Gilca, Rodica and Amini, Rachid and Carazo, Sara and Doggui, Radhouene and Frenette, Charles and Boivin, Guy and Charest, Hugues and Dumaresq, Jeannot", title="The Changing Landscape of Respiratory Viruses Contributing to Hospitalizations in Quebec, Canada: Results From an Active Hospital-Based Surveillance Study", journal="JMIR Public Health Surveill", year="2024", month="May", day="6", volume="10", pages="e40792", keywords="respiratory viruses", keywords="SARS-CoV-2", keywords="COVID-19", keywords="hospitalizations", keywords="acute respiratory infections", keywords="children", keywords="adults", keywords="coinfections", keywords="prepandemic", keywords="pandemic", abstract="Background: A comprehensive description of the combined effect of SARS-CoV-2 and respiratory viruses other than SARS-CoV-2 (ORVs) on acute respiratory infection (ARI) hospitalizations is lacking. Objective: This study aimed to compare the viral etiology of ARI hospitalizations before the pandemic (8 prepandemic influenza seasons, 2012-13 to 2019-20) and during 3 pandemic years (periods of increased SARS-CoV-2 and ORV circulation in 2020-21, 2021-22, and 2022-23) from an active hospital-based surveillance network in Quebec, Canada. Methods: We compared the detection of ORVs and SARS-CoV-2 during 3 pandemic years to that in 8 prepandemic influenza seasons among patients hospitalized with ARI who were tested systematically by the same multiplex polymerase chain reaction (PCR) assay during periods of intense respiratory virus (RV) circulation. The proportions of infections between prepandemic and pandemic years were compared by using appropriate statistical tests. Results: During prepandemic influenza seasons, overall RV detection was 92.7\% (1384/1493) (respiratory syncytial virus [RSV]: 721/1493, 48.3\%; coinfections: 456/1493, 30.5\%) in children (<18 years) and 62.8\% (2723/4339) (influenza: 1742/4339, 40.1\%; coinfections: 264/4339, 6.1\%) in adults. Overall RV detection in children was lower during pandemic years but increased from 58.6\% (17/29) in 2020-21 (all ORVs; coinfections: 7/29, 24.1\%) to 90.3\% (308/341) in 2021-22 (ORVs: 278/341, 82\%; SARS-CoV-2: 30/341, 8.8\%; coinfections: 110/341, 32.3\%) and 88.9\% (361/406) in 2022-23 (ORVs: 339/406, 84\%; SARS-CoV-2: 22/406, 5.4\%; coinfections: 128/406, 31.5\%). In adults, overall RV detection was also lower during pandemic years but increased from 43.7\% (333/762) in 2020-21 (ORVs: 26/762, 3.4\%; SARS-CoV-2: 307/762, 40.3\%; coinfections: 7/762, 0.9\%) to 57.8\% (731/1265) in 2021-22 (ORVs: 179/1265, 14.2\%; SARS-CoV-2: 552/1265, 43.6\%; coinfections: 42/1265, 3.3\%) and 50.1\% (746/1488) in 2022-23 (ORVs: 409/1488, 27.5\%; SARS-CoV-2: 337/1488, 22.6\%; coinfections: 36/1488, 2.4\%). No influenza or RSV was detected in 2020-21; however, their detection increased in the 2 subsequent years but did not reach prepandemic levels. Compared to the prepandemic period, the peaks of RSV hospitalization shifted in 2021-22 (16 weeks earlier) and 2022-23 (15 weeks earlier). Moreover, the peaks of influenza hospitalization shifted in 2021-22 (17 weeks later) and 2022-23 (4 weeks earlier). Age distribution was different compared to the prepandemic period, especially during the first pandemic year. Conclusions: Significant shifts in viral etiology, seasonality, and age distribution of ARI hospitalizations occurred during the 3 pandemic years. Changes in age distribution observed in our study may reflect modifications in the landscape of circulating RVs and their contribution to ARI hospitalizations. During the pandemic period, SARS-CoV-2 had a low contribution to pediatric ARI hospitalizations, while it was the main contributor to adult ARI hospitalizations during the first 2 seasons and dropped below ORVs during the third pandemic season. Evolving RVs epidemiology underscores the need for increased scrutiny of ARI hospitalization etiology to inform tailored public health recommendations. ", doi="10.2196/40792", url="https://publichealth.jmir.org/2024/1/e40792", url="http://www.ncbi.nlm.nih.gov/pubmed/38709551" } @Article{info:doi/10.2196/54340, author="Borchering, K. Rebecca and Biggerstaff, Matthew and Brammer, Lynnette and Budd, Alicia and Garg, Shikha and Fry, M. Alicia and Iuliano, Danielle A. and Reed, Carrie", title="Responding to the Return of Influenza in the United States by Applying Centers for Disease Control and Prevention Surveillance, Analysis, and Modeling to Inform Understanding of Seasonal Influenza", journal="JMIR Public Health Surveill", year="2024", month="Apr", day="8", volume="10", pages="e54340", keywords="disease burden", keywords="modeling", keywords="seasonal influenza", keywords="surveillance", doi="10.2196/54340", url="https://publichealth.jmir.org/2024/1/e54340", url="http://www.ncbi.nlm.nih.gov/pubmed/38587882" } @Article{info:doi/10.2196/52047, author="Gu, Xinchun and Watson, Conall and Agrawal, Utkarsh and Whitaker, Heather and Elson, H. William and Anand, Sneha and Borrow, Ray and Buckingham, Anna and Button, Elizabeth and Curtis, Lottie and Dunn, Dominic and Elliot, J. Alex and Ferreira, Filipa and Goudie, Rosalind and Hoang, Uy and Hoschler, Katja and Jamie, Gavin and Kar, Debasish and Kele, Beatrix and Leston, Meredith and Linley, Ezra and Macartney, Jack and Marsden, L. Gemma and Okusi, Cecilia and Parvizi, Omid and Quinot, Catherine and Sebastianpillai, Praveen and Sexton, Vanashree and Smith, Gillian and Suli, Timea and Thomas, B. Nicholas P. and Thompson, Catherine and Todkill, Daniel and Wimalaratna, Rashmi and Inada-Kim, Matthew and Andrews, Nick and Tzortziou-Brown, Victoria and Byford, Rachel and Zambon, Maria and Lopez-Bernal, Jamie and de Lusignan, Simon", title="Postpandemic Sentinel Surveillance of Respiratory Diseases in the Context of the World Health Organization Mosaic Framework: Protocol for a Development and Evaluation Study Involving the English Primary Care Network 2023-2024", journal="JMIR Public Health Surveill", year="2024", month="Apr", day="3", volume="10", pages="e52047", keywords="sentinel surveillance", keywords="pandemic", keywords="COVID-19", keywords="human influenza", keywords="influenza vaccines", keywords="respiratory tract infections", keywords="vaccination", keywords="World Health Organization", keywords="respiratory syncytial virus", keywords="phenotype", keywords="computerized medical record system", abstract="Background: Prepandemic sentinel surveillance focused on improved management of winter pressures, with influenza-like illness (ILI) being the key clinical indicator. The World Health Organization (WHO) global standards for influenza surveillance include monitoring acute respiratory infection (ARI) and ILI. The WHO's mosaic framework recommends that the surveillance strategies of countries include the virological monitoring of respiratory viruses with pandemic potential such as influenza. The Oxford-Royal College of General Practitioner Research and Surveillance Centre (RSC) in collaboration with the UK Health Security Agency (UKHSA) has provided sentinel surveillance since 1967, including virology since 1993. Objective: We aim to describe the RSC's plans for sentinel surveillance in the 2023-2024 season and evaluate these plans against the WHO mosaic framework. Methods: Our approach, which includes patient and public involvement, contributes to surveillance objectives across all 3 domains of the mosaic framework. We will generate an ARI phenotype to enable reporting of this indicator in addition to ILI. These data will support UKHSA's sentinel surveillance, including vaccine effectiveness and burden of disease studies. The panel of virology tests analyzed in UKHSA's reference laboratory will remain unchanged, with additional plans for point-of-care testing, pneumococcus testing, and asymptomatic screening. Our sampling framework for serological surveillance will provide greater representativeness and more samples from younger people. We will create a biomedical resource that enables linkage between clinical data held in the RSC and virology data, including sequencing data, held by the UKHSA. We describe the governance framework for the RSC. Results: We are co-designing our communication about data sharing and sampling, contextualized by the mosaic framework, with national and general practice patient and public involvement groups. We present our ARI digital phenotype and the key data RSC network members are requested to include in computerized medical records. We will share data with the UKHSA to report vaccine effectiveness for COVID-19 and influenza, assess the disease burden of respiratory syncytial virus, and perform syndromic surveillance. Virological surveillance will include COVID-19, influenza, respiratory syncytial virus, and other common respiratory viruses. We plan to pilot point-of-care testing for group A streptococcus, urine tests for pneumococcus, and asymptomatic testing. We will integrate test requests and results with the laboratory-computerized medical record system. A biomedical resource will enable research linking clinical data to virology data. The legal basis for the RSC's pseudonymized data extract is The Health Service (Control of Patient Information) Regulations 2002, and all nonsurveillance uses require research ethics approval. Conclusions: The RSC extended its surveillance activities to meet more but not all of the mosaic framework's objectives. We have introduced an ARI indicator. We seek to expand our surveillance scope and could do more around transmissibility and the benefits and risks of nonvaccine therapies. ", doi="10.2196/52047", url="https://publichealth.jmir.org/2024/1/e52047", url="http://www.ncbi.nlm.nih.gov/pubmed/38569175" } @Article{info:doi/10.2196/50799, author="Owusu, Daniel and Ndegwa, K. Linus and Ayugi, Jorim and Kinuthia, Peter and Kalani, Rosalia and Okeyo, Mary and Otieno, A. Nancy and Kikwai, Gilbert and Juma, Bonventure and Munyua, Peninah and Kuria, Francis and Okunga, Emmanuel and Moen, C. Ann and Emukule, O. Gideon", title="Use of Sentinel Surveillance Platforms for Monitoring SARS-CoV-2 Activity: Evidence From Analysis of Kenya Influenza Sentinel Surveillance Data", journal="JMIR Public Health Surveill", year="2024", month="Mar", day="25", volume="10", pages="e50799", keywords="SARS-CoV-2", keywords="COVID-19", keywords="influenza", keywords="sentinel surveillance", keywords="Kenya", keywords="epidemic", keywords="local outbreak", keywords="respiratory infection", keywords="surveillance", keywords="cocirculation", keywords="monitoring", keywords="respiratory pathogen", abstract="Background: Little is known about the cocirculation of influenza and SARS-CoV-2 viruses during the COVID-19 pandemic and the use of respiratory disease sentinel surveillance platforms for monitoring SARS-CoV-2 activity in sub-Saharan Africa. Objective: We aimed to describe influenza and SARS-CoV-2 cocirculation in Kenya and how the SARS-CoV-2 data from influenza sentinel surveillance correlated with that of universal national surveillance. Methods: From April 2020 to March 2022, we enrolled 7349 patients with severe acute respiratory illness or influenza-like illness at 8 sentinel influenza surveillance sites in Kenya and collected demographic, clinical, underlying medical condition, vaccination, and exposure information, as well as respiratory specimens, from them. Respiratory specimens were tested for influenza and SARS-CoV-2 by real-time reverse transcription polymerase chain reaction. The universal national-level SARS-CoV-2 data were also obtained from the Kenya Ministry of Health. The universal national-level SARS-CoV-2 data were collected from all health facilities nationally, border entry points, and contact tracing in Kenya. Epidemic curves and Pearson r were used to describe the correlation between SARS-CoV-2 positivity in data from the 8 influenza sentinel sites in Kenya and that of the universal national SARS-CoV-2 surveillance data. A logistic regression model was used to assess the association between influenza and SARS-CoV-2 coinfection with severe clinical illness. We defined severe clinical illness as any of oxygen saturation <90\%, in-hospital death, admission to intensive care unit or high dependence unit, mechanical ventilation, or a report of any danger sign (ie, inability to drink or eat, severe vomiting, grunting, stridor, or unconsciousness in children younger than 5 years) among patients with severe acute respiratory illness. Results: Of the 7349 patients from the influenza sentinel surveillance sites, 76.3\% (n=5606) were younger than 5 years. We detected any influenza (A or B) in 8.7\% (629/7224), SARS-CoV-2 in 10.7\% (768/7199), and coinfection in 0.9\% (63/7165) of samples tested. Although the number of samples tested for SARS-CoV-2 from the sentinel surveillance was only 0.2\% (60 per week vs 36,000 per week) of the number tested in the universal national surveillance, SARS-CoV-2 positivity in the sentinel surveillance data significantly correlated with that of the universal national surveillance (Pearson r=0.58; P<.001). The adjusted odds ratios (aOR) of clinical severe illness among participants with coinfection were similar to those of patients with influenza only (aOR 0.91, 95\% CI 0.47-1.79) and SARS-CoV-2 only (aOR 0.92, 95\% CI 0.47-1.82). Conclusions: Influenza substantially cocirculated with SARS-CoV-2 in Kenya. We found a significant correlation of SARS-CoV-2 positivity in the data from 8 influenza sentinel surveillance sites with that of the universal national SARS-CoV-2 surveillance data. Our findings indicate that the influenza sentinel surveillance system can be used as a sustainable platform for monitoring respiratory pathogens of pandemic potential or public health importance. ", doi="10.2196/50799", url="https://publichealth.jmir.org/2024/1/e50799", url="http://www.ncbi.nlm.nih.gov/pubmed/38526537" } @Article{info:doi/10.2196/40216, author="Gertz, Autumn and Rader, Benjamin and Sewalk, Kara and Varrelman, J. Tanner and Smolinski, Mark and Brownstein, S. John", title="Decreased Seasonal Influenza Rates Detected in a Crowdsourced Influenza-Like Illness Surveillance System During the COVID-19 Pandemic: Prospective Cohort Study", journal="JMIR Public Health Surveill", year="2023", month="Dec", day="28", volume="9", pages="e40216", keywords="participatory surveillance", keywords="influenza", keywords="crowdsourced data", keywords="disease surveillance", keywords="surveillance", keywords="COVID-19", keywords="respiratory", keywords="transmission", keywords="detection", keywords="survey", keywords="sore throat", keywords="fever", keywords="cough", keywords="vaccination", keywords="diagnosis", keywords="precautions", abstract="Background: Seasonal respiratory viruses had lower incidence during their 2019-2020 and 2020-2021 seasons, which overlapped with the COVID-19 pandemic. The widespread implementation of precautionary measures to prevent transmission of SARS-CoV-2 has been seen to also mitigate transmission of seasonal influenza. The COVID-19 pandemic also led to changes in care seeking and access. Participatory surveillance systems have historically captured mild illnesses that are often missed by surveillance systems that rely on encounters with a health care provider for detection. Objective: This study aimed to assess if a crowdsourced syndromic surveillance system capable of detecting mild influenza-like illness (ILI) also captured the globally observed decrease in ILI in the 2019-2020 and 2020-2021 influenza seasons, concurrent with the COVID-19 pandemic. Methods: Flu Near You (FNY) is a web-based participatory syndromic surveillance system that allows participants in the United States to report their health information using a brief weekly survey. Reminder emails are sent to registered FNY participants to report on their symptoms and the symptoms of household members. Guest participants may also report. ILI was defined as fever and sore throat or fever and cough. ILI rates were determined as the number of ILI reports over the total number of reports and assessed for the 2016-2017, 2017-2018, 2018-2019, 2019-2020, and 2020-2021 influenza seasons. Baseline season (2016-2017, 2017-2018, and 2018-2019) rates were compared to the 2019-2020 and 2020-2021 influenza seasons. Self-reported influenza diagnosis and vaccination status were captured and assessed as the total number of reported events over the total number of reports submitted. CIs for all proportions were calculated via a 1-sample test of proportions. Results: ILI was detected in 3.8\% (32,239/848,878) of participants in the baseline seasons (2016-2019), 2.58\% (7418/287,909) in the 2019-2020 season, and 0.27\% (546/201,079) in the 2020-2021 season. Both influenza seasons that overlapped with the COVID-19 pandemic had lower ILI rates than the baseline seasons. ILI decline was observed during the months with widespread implementation of COVID-19 precautions, starting in February 2020. Self-reported influenza diagnoses decreased from early 2020 through the influenza season. Self-reported influenza positivity among ILI cases varied over the observed time period. Self-reported influenza vaccination rates in FNY were high across all observed seasons. Conclusions: A decrease in ILI was detected in the crowdsourced FNY surveillance system during the 2019-2020 and 2020-2021 influenza seasons, mirroring trends observed in other influenza surveillance systems. Specifically, the months within seasons that overlapped with widespread pandemic precautions showed decreases in ILI and confirmed influenza. Concerns persist regarding respiratory pathogens re-emerging with changes to COVID-19 guidelines. Traditional surveillance is subject to changes in health care behaviors. Systems like FNY are uniquely situated to detect disease across disease severity and care seeking, providing key insights during public health emergencies. ", doi="10.2196/40216", url="https://publichealth.jmir.org/2023/1/e40216", url="http://www.ncbi.nlm.nih.gov/pubmed/38153782" } @Article{info:doi/10.2196/45085, author="Yang, Liuyang and Zhang, Ting and Han, Xuan and Yang, Jiao and Sun, Yanxia and Ma, Libing and Chen, Jialong and Li, Yanming and Lai, Shengjie and Li, Wei and Feng, Luzhao and Yang, Weizhong", title="Influenza Epidemic Trend Surveillance and Prediction Based on Search Engine Data: Deep Learning Model Study", journal="J Med Internet Res", year="2023", month="Oct", day="17", volume="25", pages="e45085", keywords="early warning", keywords="epidemic intelligence", keywords="infectious disease", keywords="influenza-like illness", keywords="surveillance", abstract="Background: Influenza outbreaks pose a significant threat to global public health. Traditional surveillance systems and simple algorithms often struggle to predict influenza outbreaks in an accurate and timely manner. Big data and modern technology have offered new modalities for disease surveillance and prediction. Influenza-like illness can serve as a valuable surveillance tool for emerging respiratory infectious diseases like influenza and COVID-19, especially when reported case data may not fully reflect the actual epidemic curve. Objective: This study aimed to develop a predictive model for influenza outbreaks by combining Baidu search query data with traditional virological surveillance data. The goal was to improve early detection and preparedness for influenza outbreaks in both northern and southern China, providing evidence for supplementing modern intelligence epidemic surveillance methods. Methods: We collected virological data from the National Influenza Surveillance Network and Baidu search query data from January 2011 to July 2018, totaling 3,691,865 and 1,563,361 respective samples. Relevant search terms related to influenza were identified and analyzed for their correlation with influenza-positive rates using Pearson correlation analysis. A distributed lag nonlinear model was used to assess the lag correlation of the search terms with influenza activity. Subsequently, a predictive model based on the gated recurrent unit and multiple attention mechanisms was developed to forecast the influenza-positive trend. Results: This study revealed a high correlation between specific Baidu search terms and influenza-positive rates in both northern and southern China, except for 1 term. The search terms were categorized into 4 groups: essential facts on influenza, influenza symptoms, influenza treatment and medicine, and influenza prevention, all of which showed correlation with the influenza-positive rate. The influenza prevention and influenza symptom groups had a lag correlation of 1.4-3.2 and 5.0-8.0 days, respectively. The Baidu search terms could help predict the influenza-positive rate 14-22 days in advance in southern China but interfered with influenza surveillance in northern China. Conclusions: Complementing traditional disease surveillance systems with information from web-based data sources can aid in detecting warning signs of influenza outbreaks earlier. However, supplementation of modern surveillance with search engine information should be approached cautiously. This approach provides valuable insights for digital epidemiology and has the potential for broader application in respiratory infectious disease surveillance. Further research should explore the optimization and customization of search terms for different regions and languages to improve the accuracy of influenza prediction models. ", doi="10.2196/45085", url="https://www.jmir.org/2023/1/e45085", url="http://www.ncbi.nlm.nih.gov/pubmed/37847532" } @Article{info:doi/10.2196/46644, author="Leal Neto, Onicio and Paolotti, Daniela and Dalton, Craig and Carlson, Sandra and Susumpow, Patipat and Parker, Matt and Phetra, Polowat and Lau, Y. Eric H. and Colizza, Vittoria and Jan van Hoek, Albert and Kjels{\o}, Charlotte and Brownstein, S. John and Smolinski, S. Mark", title="Enabling Multicentric Participatory Disease Surveillance for Global Health Enhancement: Viewpoint on Global Flu View", journal="JMIR Public Health Surveill", year="2023", month="Sep", day="1", volume="9", pages="e46644", keywords="participatory surveillance", keywords="digital epidemiology", keywords="influenza-like illness", keywords="data transfer", keywords="surveillance", keywords="digital platform", keywords="Global Flu View", keywords="program", keywords="data sharing", keywords="public health", keywords="innovative", keywords="flu", doi="10.2196/46644", url="https://publichealth.jmir.org/2023/1/e46644", url="http://www.ncbi.nlm.nih.gov/pubmed/37490846" } @Article{info:doi/10.2196/46383, author="Szablewski, M. Christine and Iwamoto, Chelsea and Olsen, J. Sonja and Greene, M. Carolyn and Duca, M. Lindsey and Davis, Todd C. and Coggeshall, C. Kira and Davis, W. William and Emukule, O. Gideon and Gould, L. Philip and Fry, M. Alicia and Wentworth, E. David and Dugan, G. Vivien and Kile, C. James and Azziz-Baumgartner, Eduardo", title="Reported Global Avian Influenza Detections Among Humans and Animals During 2013-2022: Comprehensive Review and Analysis of Available Surveillance Data", journal="JMIR Public Health Surveill", year="2023", month="Aug", day="31", volume="9", pages="e46383", keywords="avian influenza", keywords="novel influenza", keywords="pandemic influenza", keywords="One Health", keywords="zoonotic influenza", keywords="surveillance", abstract="Background: Avian influenza (AI) virus detections occurred frequently in 2022 and continue to pose a health, economic, and food security risk. The most recent global analysis of official reports of animal outbreaks and human infections with all reportable AI viruses was published almost a decade ago. Increased or renewed reports of AI viruses, especially high pathogenicity H5N8 and H5N1 in birds and H5N1, H5N8, and H5N6 in humans globally, have established the need for a comprehensive review of current global AI virus surveillance data to assess the pandemic risk of AI viruses. Objective: This study aims to provide an analysis of global AI animal outbreak and human case surveillance information from the last decade by describing the circulating virus subtypes, regions and temporal trends in reporting, and country characteristics associated with AI virus outbreak reporting in animals; surveillance and reporting gaps for animals and humans are identified. Methods: We analyzed AI virus infection reports among animals and humans submitted to animal and public health authorities from January 2013 to June 2022 and compared them with reports from January 2005 to December 2012. A multivariable regression analysis was used to evaluate associations between variables of interest and reported AI virus animal outbreaks. Results: From 2013 to 2022, 52.2\% (95/182) of World Organisation for Animal Health (WOAH) Member Countries identified 34 AI virus subtypes during 21,249 outbreaks. The most frequently reported subtypes were high pathogenicity AI H5N1 (10,079/21,249, 47.43\%) and H5N8 (6722/21,249, 31.63\%). A total of 10 high pathogenicity AI and 6 low pathogenicity AI virus subtypes were reported to the WOAH for the first time during 2013-2022. AI outbreaks in animals occurred in 26 more Member Countries than reported in the previous 8 years. Decreasing World Bank income classification was significantly associated with decreases in reported AI outbreaks (P<.001-.02). Between January 2013 and June 2022, 17/194 (8.8\%) World Health Organization (WHO) Member States reported 2000 human AI virus infections of 10 virus subtypes. H7N9 (1568/2000, 78.40\%) and H5N1 (254/2000, 12.70\%) viruses accounted for the most human infections. As many as 8 of these 17 Member States did not report a human case prior to 2013. Of 1953 human cases with available information, 74.81\% (n=1461) had a known animal exposure before onset of illness. The median time from illness onset to the notification posted on the WHO event information site was 15 days (IQR 9-30 days; mean 24 days). Seasonality patterns of animal outbreaks and human infections with AI viruses were very similar, occurred year-round, and peaked during November through May. Conclusions: Our analysis suggests that AI outbreaks are more frequently reported and geographically widespread than in the past. Global surveillance gaps include inconsistent reporting from all regions and human infection reporting delays. Continued monitoring for AI virus outbreaks in animals and human infections with AI viruses is crucial for pandemic preparedness. ", doi="10.2196/46383", url="https://publichealth.jmir.org/2023/1/e46383", url="http://www.ncbi.nlm.nih.gov/pubmed/37651182" } @Article{info:doi/10.2196/41435, author="Lei, Hao and Zhang, Nan and Niu, Beidi and Wang, Xiao and Xiao, Shenglan and Du, Xiangjun and Chen, Tao and Yang, Lei and Wang, Dayan and Cowling, Benjamin and Li, Yuguo and Shu, Yuelong", title="Effect of Rapid Urbanization in Mainland China on the Seasonal Influenza Epidemic: Spatiotemporal Analysis of Surveillance Data From 2010 to 2017", journal="JMIR Public Health Surveill", year="2023", month="Jul", day="7", volume="9", pages="e41435", keywords="seasonal influenza", keywords="attack rate", keywords="urbanization", keywords="urban population", keywords="human contact", keywords="agent-based model", keywords="influenza", keywords="seasonal flu", keywords="spatiotemporal", keywords="epidemic", keywords="disease transmission", keywords="disease spread", keywords="epidemiology", keywords="influenza transmission", keywords="epidemics", abstract="Background: The world is undergoing an unprecedented wave of urbanization. However, the effect of rapid urbanization during the early or middle stages of urbanization on seasonal influenza transmission remains unknown. Since about 70\% of the world population live in low-income countries, exploring the impact of urbanization on influenza transmission in urbanized countries is significant for global infection prediction and prevention. Objective: The aim of this study was to explore the effect of rapid urbanization on influenza transmission in China. Methods: We performed spatiotemporal analyses of province-level influenza surveillance data collected in Mainland China from April 1, 2010, to March 31, 2017. An agent-based model based on hourly human contact--related behaviors was built to simulate the influenza transmission dynamics and to explore the potential mechanism of the impact of urbanization on influenza transmission. Results: We observed persistent differences in the influenza epidemic attack rates among the provinces of Mainland China across the 7-year study period, and the attack rate in the winter waves exhibited a U-shaped relationship with the urbanization rates, with a turning point at 50\%-60\% urbanization across Mainland China. Rapid Chinese urbanization has led to increases in the urban population density and percentage of the workforce but decreases in household size and the percentage of student population. The net effect of increased influenza transmission in the community and workplaces but decreased transmission in households and schools yielded the observed U-shaped relationship. Conclusions: Our results highlight the complicated effects of urbanization on the seasonal influenza epidemic in China. As the current urbanization rate in China is approximately 59\%, further urbanization with no relevant interventions suggests a worrisome increasing future trend in the influenza epidemic attack rate. ", doi="10.2196/41435", url="https://publichealth.jmir.org/2023/1/e41435", url="http://www.ncbi.nlm.nih.gov/pubmed/37418298" } @Article{info:doi/10.2196/44970, author="Wang, Qing and Jia, Mengmeng and Jiang, Mingyue and Liu, Wei and Yang, Jin and Dai, Peixi and Sun, Yanxia and Qian, Jie and Yang, Weizhong and Feng, Luzhao", title="Seesaw Effect Between COVID-19 and Influenza From 2020 to 2023 in World Health Organization Regions: Correlation Analysis", journal="JMIR Public Health Surveill", year="2023", month="Jun", day="12", volume="9", pages="e44970", keywords="COVID-19", keywords="influenza", keywords="negative correlation", keywords="seesaw effect", keywords="respiratory infectious disease", keywords="epidemiological trends", abstract="Background: Seasonal influenza activity showed a sharp decline in activity at the beginning of the emergence of COVID-19. Whether there is an epidemiological correlation between the dynamic of these 2 respiratory infectious diseases and their future trends needs to be explored. Objective: We aimed to assess the correlation between COVID-19 and influenza activity and estimate later epidemiological trends. Methods: We retrospectively described the dynamics of COVID-19 and influenza in 6 World Health Organization (WHO) regions from January 2020 to March 2023 and used the long short-term memory machine learning model to learn potential patterns in previously observed activity and predict trends for the following 16 weeks. Finally, we used Spearman correlation coefficients to assess the past and future epidemiological correlation between these 2 respiratory infectious diseases. Results: With the emergence of the original strain of SARS-CoV-2 and other variants, influenza activity stayed below 10\% for more than 1 year in the 6 WHO regions. Subsequently, it gradually rose as Delta activity dropped, but still peaked below Delta. During the Omicron pandemic and the following period, the activity of each disease increased as the other decreased, alternating in dominance more than once, with each alternation lasting for 3 to 4 months. Correlation analysis showed that COVID-19 and influenza activity presented a predominantly negative correlation, with coefficients above --0.3 in WHO regions, especially during the Omicron pandemic and the following estimated period. The diseases had a transient positive correlation in the European region of the WHO and the Western Pacific region of the WHO when multiple dominant strains created a mixed pandemic. Conclusions: Influenza activity and past seasonal epidemiological patterns were shaken by the COVID-19 pandemic. The activity of these diseases was moderately or greater than moderately inversely correlated, and they suppressed and competed with each other, showing a seesaw effect. In the postpandemic era, this seesaw trend may be more prominent, suggesting the possibility of using one disease as an early warning signal for the other when making future estimates and conducting optimized annual vaccine campaigns. ", doi="10.2196/44970", url="https://publichealth.jmir.org/2023/1/e44970", url="http://www.ncbi.nlm.nih.gov/pubmed/37191650" } @Article{info:doi/10.2196/39700, author="Bota, Brianne A. and Bettinger, A. Julie and Sarfo-Mensah, Shirley and Lopez, Jimmy and Smith, P. David and Atkinson, M. Katherine and Bell, Cameron and Marty, Kim and Serhan, Mohamed and Zhu, T. David and McCarthy, E. Anne and Wilson, Kumanan", title="Comparing the Use of a Mobile App and a Web-Based Notification Platform for Surveillance of Adverse Events Following Influenza Immunization: Randomized Controlled Trial", journal="JMIR Public Health Surveill", year="2023", month="May", day="8", volume="9", pages="e39700", keywords="active participant--centered reporting", keywords="health technology", keywords="adverse event reporting", keywords="mobile apps", keywords="immunization", keywords="vaccine", keywords="safety", keywords="influenza", keywords="campaign", keywords="apps", keywords="mobile", keywords="surveillance", keywords="pharmacovigilance", abstract="Background: Vaccine safety surveillance is a core component of?vaccine pharmacovigilance. In Canada, active, participant-centered vaccine surveillance is available for influenza vaccines and has been used for COVID-19 vaccines. Objective: The objective of this study is to evaluate the effectiveness and feasibility of using a mobile app for reporting participant-centered seasonal influenza adverse events following immunization (AEFIs) compared to a web-based notification system. Methods: Participants were randomized to influenza vaccine safety reporting via a mobile app or a web-based notification platform. All participants were invited to complete a user experience survey. Results: Among the 2408 randomized participants, 1319 (54\%) completed their safety survey 1 week after vaccination, with a higher completion rate among the web-based notification platform users (767/1196, 64\%) than among mobile app users (552/1212, 45\%; P<.001). Ease-of-use ratings were high for the web-based notification platform users (99\% strongly agree or agree) and 88.8\% of them strongly agreed or agreed that the system made reporting AEFIs easier. Web-based notification platform users supported the statement that a web-based notification-only approach would make it easier for public health professionals to detect vaccine safety signals (91.4\%, agreed or strongly agreed). Conclusions: Participants in this study were significantly more likely to respond to a web-based safety survey rather than within a mobile app. These results suggest that mobile apps present an additional barrier for use compared to the web-based notification--only approach. Trial Registration: ClinicalTrials.gov NCT05794113; https://clinicaltrials.gov/show/NCT05794113 ", doi="10.2196/39700", url="https://publichealth.jmir.org/2023/1/e39700", url="http://www.ncbi.nlm.nih.gov/pubmed/37155240" } @Article{info:doi/10.2196/41050, author="Hunter, Victoria and Shapiro, Allison and Chawla, Devika and Drawnel, Faye and Ramirez, Ernesto and Phillips, Elizabeth and Tadesse-Bell, Sara and Foschini, Luca and Ukachukwu, Vincent", title="Characterization of Influenza-Like Illness Burden Using Commercial Wearable Sensor Data and Patient-Reported Outcomes: Mixed Methods Cohort Study", journal="J Med Internet Res", year="2023", month="Mar", day="23", volume="25", pages="e41050", keywords="influenza", keywords="influenza-like illness", keywords="wearable sensor", keywords="person-generated health care data", abstract="Background: The burden of influenza-like illness (ILI) is typically estimated via hospitalizations and deaths. However, ILI-associated morbidity that does not require hospitalization remains poorly characterized. Objective: The main objective of this study was to characterize ILI burden using commercial wearable sensor data and investigate the extent to which these data correlate with self-reported illness severity and duration. Furthermore, we aimed to determine whether ILI-associated changes in wearable sensor data differed between care-seeking and non--care-seeking populations as well as between those with confirmed influenza infection and those with ILI symptoms only. Methods: This study comprised participants enrolled in either the FluStudy2020 or the Home Testing of Respiratory Illness (HTRI) study; both studies were similar in design and conducted between December 2019 and October 2020 in the United States. The participants self-reported ILI-related symptoms and health care--seeking behaviors via daily, biweekly, and monthly surveys. Wearable sensor data were recorded for 120 and 150 days for FluStudy2020 and HTRI, respectively. The following features were assessed: total daily steps, active time (time spent with >50 steps per minute), sleep duration, sleep efficiency, and resting heart rate. ILI-related changes in wearable sensor data were compared between the participants who sought health care and those who did not and between the participants who tested positive for influenza and those with symptoms only. Correlative analyses were performed between wearable sensor data and patient-reported outcomes. Results: After combining the FluStudy2020 and HTRI data sets, the final ILI population comprised 2435 participants. Compared with healthy days (baseline), the participants with ILI exhibited significantly reduced total daily steps, active time, and sleep efficiency as well as increased sleep duration and resting heart rate. Deviations from baseline typically began before symptom onset and were greater in the participants who sought health care than in those who did not and greater in the participants who tested positive for influenza than in those with symptoms only. During an ILI event, changes in wearable sensor data consistently varied with those in patient-reported outcomes. Conclusions: Our results underscore the potential of wearable sensors to discriminate not only between individuals with and without influenza infections but also between care-seeking and non--care-seeking populations, which may have future application in health care resource planning. Trial Registration: Clinicaltrials.gov NCT04245800; https://clinicaltrials.gov/ct2/show/NCT04245800 ", doi="10.2196/41050", url="https://www.jmir.org/2023/1/e41050", url="http://www.ncbi.nlm.nih.gov/pubmed/36951890" } @Article{info:doi/10.2196/38080, author="Quinn, Emma and Hsiao, Hsun Kai and Johnstone, Travers and Gomez, Maria and Parasuraman, Arun and Ingleton, Andrew and Hirst, Nicholas and Najjar, Zeina and Gupta, Leena", title="Protecting Older Adult Residents in Care Facilities Against Influenza and COVID-19 Using the Influenza Communication, Advice and Reporting (FluCARE) App: Prospective Cohort Mixed Methods Study", journal="JMIR Form Res", year="2023", month="Mar", day="13", volume="7", pages="e38080", keywords="web app", keywords="digital health", keywords="influenza", keywords="COVID-19", keywords="outbreak", keywords="monitoring", keywords="disease control", keywords="infection spread", keywords="infection control", keywords="detect", keywords="aged care", keywords="elderly", keywords="elderly population", keywords="older adult", keywords="long term care", keywords="care home", keywords="AFC", keywords="LTC", keywords="nursing home", keywords="retirement home", keywords="mobile application", keywords="health application", keywords="mHealth", keywords="care facility", keywords="online training", keywords="health impact", keywords="feasibility", keywords="efficacy", keywords="satisfaction", keywords="prevention", keywords="disease spread", keywords="notification", abstract="Background: Early detection and response to influenza and COVID-19 outbreaks in aged care facilities (ACFs) are critical to minimizing health impacts. The Sydney Local Health District (SLHD) Public Health Unit (PHU) has developed and implemented a novel web-based app with integrated functions for online line listings, detection algorithms, and automatic notifications to responders, to assist ACFs in outbreak response. The goal of the Influenza Outbreak Communication, Advice and Reporting (FluCARE) app is to reduce time delays to notifications, which we hope will reduce the spread, duration, and health impacts of an influenza or COVID-19 outbreak, as well as ease workload burdens on ACF staff. Objective: The specific aims of the study were to (1) evaluate the acceptability and user satisfaction of the implementation and use of FluCARE in helping ACFs recognize, notify, and manage influenza and COVID-19 outbreaks in their facility; (2) identify the safety of FluCARE and any potential adverse outcomes of using the app; and (3) identify any perceived barriers or facilitators to the implementation and use of FluCARE from the ACF user perspective. Methods: The FluCARE app was piloted from September 2019 to December 2020 in the SLHD. Associated implementation included promotion and engagement, user training, and operational policies. Participating ACF staff were invited to complete a posttraining survey. Staff were also invited to complete a postpilot evaluation survey that included the user Mobile Application Rating Scale (uMARS) measuring app acceptance, utility, and barriers and facilitators to use. An issues log was also prospectively maintained to assess safety. Survey data were analyzed descriptively or via content analysis where appropriate. Results: Surveys were completed by 31 consenting users from 27 ACFs. FluCARE was rated 3.91 of 5 overall on the uMARS. Of the 31 users, 25 (80\%) would definitely use FluCARE for future outbreaks, and all users agreed that the app was useful for identifying influenza and COVID-19 outbreaks at their facilities. There were no reported critical issues with incorrect or missed outbreak detection. User training, particularly online training modules, and technical support were identified as key facilitators to FluCARE use. Conclusions: FluCARE is an acceptable, useful, and safe app to assist ACF staff with early detection and response to influenza and COVID-19 outbreaks. This study supports feasibility for ongoing implementation and efficacy evaluation, followed by scale-up into other health districts in New South Wales. ", doi="10.2196/38080", url="https://formative.jmir.org/2023/1/e38080", url="http://www.ncbi.nlm.nih.gov/pubmed/36763638" } @Article{info:doi/10.2196/44238, author="Yang, Liuyang and Li, Gang and Yang, Jin and Zhang, Ting and Du, Jing and Liu, Tian and Zhang, Xingxing and Han, Xuan and Li, Wei and Ma, Libing and Feng, Luzhao and Yang, Weizhong", title="Deep-Learning Model for Influenza Prediction From Multisource Heterogeneous Data in a Megacity: Model Development and Evaluation", journal="J Med Internet Res", year="2023", month="Feb", day="13", volume="25", pages="e44238", keywords="influenza", keywords="ILI", keywords="multisource heterogeneous data", keywords="deep learning", keywords="MAL model", keywords="megacity", abstract="Background: In megacities, there is an urgent need to establish more sensitive forecasting and early warning methods for acute respiratory infectious diseases. Existing prediction and early warning models for influenza and other acute respiratory infectious diseases have limitations and therefore there is room for improvement. Objective: The aim of this study was to explore a new and better-performing deep-learning model to predict influenza trends from multisource heterogeneous data in a megacity. Methods: We collected multisource heterogeneous data from the 26th week of 2012 to the 25th week of 2019, including influenza-like illness (ILI) cases and virological surveillance, data of climate and demography, and search engines data. To avoid collinearity, we selected the best predictor according to the weight and correlation of each factor. We established a new multiattention-long short-term memory (LSTM) deep-learning model (MAL model), which was used to predict the percentage of ILI (ILI\%) cases and the product of ILI\% and the influenza-positive rate (ILI\%{\texttimes}positive\%), respectively. We also combined the data in different forms and added several machine-learning and deep-learning models commonly used in the past to predict influenza trends for comparison. The R2 value, explained variance scores, mean absolute error, and mean square error were used to evaluate the quality of the models. Results: The highest correlation coefficients were found for the Baidu search data for ILI\% and for air quality for ILI\%{\texttimes}positive\%. We first used the MAL model to calculate the ILI\%, and then combined ILI\% with climate, demographic, and Baidu data in different forms. The ILI\%+climate+demography+Baidu model had the best prediction effect, with the explained variance score reaching 0.78, R2 reaching 0.76, mean absolute error of 0.08, and mean squared error of 0.01. Similarly, we used the MAL model to calculate the ILI\%{\texttimes}positive\% and combined this prediction with different data forms. The ILI\%{\texttimes}positive\%+climate+demography+Baidu model had the best prediction effect, with an explained variance score reaching 0.74, R2 reaching 0.70, mean absolute error of 0.02, and mean squared error of 0.02. Comparisons with random forest, extreme gradient boosting, LSTM, and gated current unit models showed that the MAL model had the best prediction effect. Conclusions: The newly established MAL model outperformed existing models. Natural factors and search engine query data were more helpful in forecasting ILI patterns in megacities. With more timely and effective prediction of influenza and other respiratory infectious diseases and the epidemic intensity, early and better preparedness can be achieved to reduce the health damage to the population. ", doi="10.2196/44238", url="https://www.jmir.org/2023/1/e44238", url="http://www.ncbi.nlm.nih.gov/pubmed/36780207" } @Article{info:doi/10.2196/42519, author="Athanasiou, Maria and Fragkozidis, Georgios and Zarkogianni, Konstantia and Nikita, S. Konstantina", title="Long Short-term Memory--Based Prediction of the Spread of Influenza-Like Illness Leveraging Surveillance, Weather, and Twitter Data: Model Development and Validation", journal="J Med Internet Res", year="2023", month="Feb", day="6", volume="25", pages="e42519", keywords="influenza-like illness", keywords="epidemiological surveillance", keywords="machine learning", keywords="deep learning", keywords="social media", keywords="Twitter", keywords="meteorological parameters", abstract="Background: The potential to harness the plurality of available data in real time along with advanced data analytics for the accurate prediction of influenza-like illness (ILI) outbreaks has gained significant scientific interest. Different methodologies based on the use of machine learning techniques and traditional and alternative data sources, such as ILI surveillance reports, weather reports, search engine queries, and social media, have been explored with the ultimate goal of being used in the development of electronic surveillance systems that could complement existing monitoring resources. Objective: The scope of this study was to investigate for the first time the combined use of ILI surveillance data, weather data, and Twitter data along with deep learning techniques toward the development of prediction models able to nowcast and forecast weekly ILI cases. By assessing the predictive power of both traditional and alternative data sources on the use case of ILI, this study aimed to provide a novel approach for corroborating evidence and enhancing accuracy and reliability in the surveillance of infectious diseases. Methods: The model's input space consisted of information related to weekly ILI surveillance, web-based social (eg, Twitter) behavior, and weather conditions. For the design and development of the model, relevant data corresponding to the period of 2010 to 2019 and focusing on the Greek population and weather were collected. Long short-term memory (LSTM) neural networks were leveraged to efficiently handle the sequential and nonlinear nature of the multitude of collected data. The 3 data categories were first used separately for training 3 LSTM-based primary models. Subsequently, different transfer learning (TL) approaches were explored with the aim of creating various feature spaces combining the features extracted from the corresponding primary models' LSTM layers for the latter to feed a dense layer. Results: The primary model that learned from weather data yielded better forecast accuracy (root mean square error [RMSE]=0.144; Pearson correlation coefficient [PCC]=0.801) than the model trained with ILI historical data (RMSE=0.159; PCC=0.794). The best performance was achieved by the TL-based model leveraging the combination of the 3 data categories (RMSE=0.128; PCC=0.822). Conclusions: The superiority of the TL-based model, which considers Twitter data, weather data, and ILI surveillance data, reflects the potential of alternative public sources to enhance accurate and reliable prediction of ILI spread. Despite its focus on the use case of Greece, the proposed approach can be generalized to other locations, populations, and social media platforms to support the surveillance of infectious diseases with the ultimate goal of reinforcing preparedness for future epidemics. ", doi="10.2196/42519", url="https://www.jmir.org/2023/1/e42519", url="http://www.ncbi.nlm.nih.gov/pubmed/36745490" } @Article{info:doi/10.2196/42530, author="Li, Li and Yan, Ze-Lin and Luo, Lei and Liu, Wenhui and Yang, Zhou and Shi, Chen and Ming, Bo-Wen and Yang, Jun and Cao, Peihua and Ou, Chun-Quan", title="Influenza-Associated Excess Mortality by Age, Sex, and Subtype/Lineage: Population-Based Time-Series Study With a Distributed-Lag Nonlinear Model", journal="JMIR Public Health Surveill", year="2023", month="Jan", day="11", volume="9", pages="e42530", keywords="influenza", keywords="disease burden", keywords="distributed-lag nonlinear model", keywords="excess mortality", keywords="harvesting effects", abstract="Background: Accurate estimation of the influenza death burden is of great significance for influenza prevention and control. However, few studies have considered the short-term harvesting effects of influenza on mortality when estimating influenza-associated excess deaths by cause of death, age, sex, and subtype/lineage. Objective: This study aimed to estimate the cause-, age-, and sex-specific excess mortality associated with influenza and its subtypes and lineages in Guangzhou from 2015 to 2018. Methods: Distributed-lag nonlinear models were fitted to estimate the excess mortality related to influenza subtypes or lineages for different causes of death, age groups, and sex based on daily time-series data for mortality, influenza, and meteorological factors. Results: A total of 199,777 death certificates were included in the study. The average annual influenza-associated excess mortality rate (EMR) was 25.06 (95\% empirical CI [eCI] 19.85-30.16) per 100,000 persons; 7142 of 8791 (81.2\%) deaths were due to respiratory or cardiovascular mortality (EMR 20.36, 95\% eCI 16.75-23.74). Excess respiratory and cardiovascular deaths in people aged 60 to 79 years and those aged ?80 years accounted for 32.9\% (2346/7142) and 63.7\% (4549/7142) of deaths, respectively. The male to female ratio (MFR) of excess death from respiratory diseases was 1.34 (95\% CI 1.17-1.54), while the MFR for excess death from cardiovascular disease was 0.72 (95\% CI 0.63-0.82). The average annual excess respiratory and cardiovascular mortality rates attributed to influenza A (H3N2), B/Yamagata, B/Victoria, and A (H1N1) were 8.47 (95\% eCI 6.60-10.30), 5.81 (95\% eCI 3.35-8.25), 3.68 (95\% eCI 0.81-6.49), and 2.83 (95\% eCI --1.26 to 6.71), respectively. Among these influenza subtypes/lineages, A (H3N2) had the highest excess respiratory and cardiovascular mortality rates for people aged 60 to 79 years (20.22, 95\% eCI 14.56-25.63) and ?80 years (180.15, 95\% eCI 130.75-227.38), while younger people were more affected by A (H1N1), with an EMR of 1.29 (95\% eCI 0.07-2.32). The mortality displacement of influenza A (H1N1), A (H3N2), and B/Yamagata was 2 to 5 days, but 5 to 13 days for B/Victoria. Conclusions: Influenza was associated with substantial mortality in Guangzhou, occurring predominantly in the elderly, even after considering mortality displacement. The mortality burden of influenza B, particularly B/Yamagata, cannot be ignored. Contrasting sex differences were found in influenza-associated excess mortality from respiratory diseases and from cardiovascular diseases; the underlying mechanisms need to be investigated in future studies. Our findings can help us better understand the magnitude and time-course of the effect of influenza on mortality and inform targeted interventions for mitigating the influenza mortality burden, such as immunizations with quadrivalent vaccines (especially for older people), behavioral campaigns, and treatment strategies. ", doi="10.2196/42530", url="https://publichealth.jmir.org/2023/1/e42530", url="http://www.ncbi.nlm.nih.gov/pubmed/36630176" } @Article{info:doi/10.2196/41329, author="Tsang, K. Tim and Huang, Xiaotong and Guo, Yiyang and Lau, Y. Eric H. and Cowling, J. Benjamin and Ip, M. Dennis K.", title="Monitoring School Absenteeism for Influenza-Like Illness Surveillance: Systematic Review and Meta-analysis", journal="JMIR Public Health Surveill", year="2023", month="Jan", day="11", volume="9", pages="e41329", keywords="influenza", keywords="surveillance", keywords="school absenteeism", keywords="monitoring", keywords="school attendance", keywords="influenza-like illness", keywords="correlation", keywords="trend", keywords="pattern", keywords="predict", keywords="prediction", keywords="influenza activity", keywords="infection", keywords="surveillance tolls", abstract="Background: Influenza causes considerable disease burden each year, particularly in children. Monitoring school absenteeism has long been proposed as a surveillance tool of influenza activity in the community, but the practice of school absenteeism could be varying, and the potential of such usage remains unclear. Objective: The aim of this paper is to determine the potential of monitoring school absenteeism as a surveillance tool of influenza. Methods: We conducted a systematic review of the published literature on the relationship between school absenteeism and influenza activity in the community. We categorized the types of school absenteeism and influenza activity in the community to determine the correlation between these data streams. We also extracted this correlation with different lags in community surveillance to determine the potential of using school absenteeism as a leading indicator of influenza activity. Results: Among the 35 identified studies, 22 (63\%), 12 (34\%), and 8 (23\%) studies monitored all-cause, illness-specific, and influenza-like illness (ILI)--specific absents, respectively, and 16 (46\%) used quantitative approaches and provided 33 estimates on the temporal correlation between school absenteeism and influenza activity in the community. The pooled estimate of correlation between school absenteeism and community surveillance without lag, with 1-week lag, and with 2-week lag were 0.44 (95\% CI 0.34, 0.53), 0.29 (95\% CI 0.15, 0.42), and 0.21 (95\% CI 0.11, 0.31), respectively. The correlation between influenza activity in the community and ILI-specific absenteeism was higher than that between influenza activity in community all-cause absenteeism. Among the 19 studies that used qualitative approaches, 15 (79\%) concluded that school absenteeism was in concordance with, coincided with, or was associated with community surveillance. Of the 35 identified studies, only 6 (17\%) attempted to predict influenza activity in the community from school absenteeism surveillance. Conclusions: There was a moderate correlation between school absenteeism and influenza activity in the community. The smaller correlation between school absenteeism and community surveillance with lag, compared to without lag, suggested that careful application was required to use school absenteeism as a leading indicator of influenza epidemics. ILI-specific absenteeism could monitor influenza activity more closely, but the required resource or school participation willingness may require careful consideration to weight against the associated costs. Further development is required to use and optimize the use of school absenteeism to predict influenza activity. In particular, the potential of using more advanced statistical models and validation of the predictions should be explored. ", doi="10.2196/41329", url="https://publichealth.jmir.org/2023/1/e41329", url="http://www.ncbi.nlm.nih.gov/pubmed/36630159" } @Article{info:doi/10.2196/38751, author="Okiyama, Sho and Fukuda, Memori and Sode, Masashi and Takahashi, Wataru and Ikeda, Masahiro and Kato, Hiroaki and Tsugawa, Yusuke and Iwagami, Masao", title="Examining the Use of an Artificial Intelligence Model to Diagnose Influenza: Development and Validation Study", journal="J Med Internet Res", year="2022", month="Dec", day="23", volume="24", number="12", pages="e38751", keywords="influenza", keywords="physical examination", keywords="pharynx", keywords="deep learning", keywords="diagnostic prediction", abstract="Background: The global burden of influenza is substantial. It is a major disease that causes annual epidemics and occasionally, pandemics. Given that influenza primarily infects the upper respiratory system, it may be possible to diagnose influenza infection by applying deep learning to pharyngeal images. Objective: We aimed to develop a deep learning model to diagnose influenza infection using pharyngeal images and clinical information. Methods: We recruited patients who visited clinics and hospitals because of influenza-like symptoms. In the training stage, we developed a diagnostic prediction artificial intelligence (AI) model based on deep learning to predict polymerase chain reaction (PCR)--confirmed influenza from pharyngeal images and clinical information. In the validation stage, we assessed the diagnostic performance of the AI model. In additional analysis, we compared the diagnostic performance of the AI model with that of 3 physicians and interpreted the AI model using importance heat maps. Results: We enrolled a total of 7831 patients at 64 hospitals between November 1, 2019, and January 21, 2020, in the training stage and 659 patients (including 196 patients with PCR-confirmed influenza) at 11 hospitals between January 25, 2020, and March 13, 2020, in the validation stage. The area under the receiver operating characteristic curve for the AI model was 0.90 (95\% CI 0.87-0.93), and its sensitivity and specificity were 76\% (70\%-82\%) and 88\% (85\%-91\%), respectively, outperforming 3 physicians. In the importance heat maps, the AI model often focused on follicles on the posterior pharyngeal wall. Conclusions: We developed the first AI model that can accurately diagnose influenza from pharyngeal images, which has the potential to help physicians to make a timely diagnosis. ", doi="10.2196/38751", url="https://www.jmir.org/2022/12/e38751", url="http://www.ncbi.nlm.nih.gov/pubmed/36374004" } @Article{info:doi/10.2196/36712, author="Giner-Soriano, Maria and de Dios, Vanessa and Ouchi, Dan and Vilaplana-Carnerero, Carles and Monteagudo, M{\`o}nica and Morros, Rosa", title="Outcomes of COVID-19 Infection in People Previously Vaccinated Against Influenza: Population-Based Cohort Study Using Primary Health Care Electronic Records", journal="JMIR Public Health Surveill", year="2022", month="Nov", day="11", volume="8", number="11", pages="e36712", keywords="SARS-CoV-2", keywords="COVID-19", keywords="influenza vaccines", keywords="pneumonia", keywords="electronic health records", keywords="primary health care", keywords="vaccination", keywords="public health", keywords="cohort study", keywords="epidemiology", keywords="eHeatlh", keywords="health outcome", keywords="mortality", abstract="Background: A possible link between influenza immunization and susceptibility to the complications of COVID-19 infection has been previously suggested owing to a boost in the immunity against SARS-CoV-2. Objective: This study aimed to investigate whether individuals with COVID-19 could have benefited from vaccination against influenza. We hypothesized that the immunity resulting from the previous influenza vaccination would boost part of the immunity against SARS-CoV-2. Methods: We performed a population-based cohort study including all patients with COVID-19 with registered entries in the primary health care (PHC) electronic records during the first wave of the COVID-19 pandemic (March 1 to June 30, 2020) in Catalonia, Spain. We compared individuals who took an influenza vaccine before being infected with COVID-19, with those who had not taken one. Data were obtained from Information System for Research in Primary Care, capturing PHC information of 5.8 million people from Catalonia. The main outcomes assessed during follow-up were a diagnosis of pneumonia, hospital admission, and mortality. Results: We included 309,039 individuals with COVID-19 and compared them on the basis of their influenza immunization status, with 114,181 (36.9\%) having been vaccinated at least once and 194,858 (63.1\%) having never been vaccinated. In total, 21,721 (19\%) vaccinated individuals and 11,000 (5.7\%) unvaccinated individuals had at least one of their outcomes assessed. Those vaccinated against influenza at any time (odds ratio [OR] 1.14, 95\% CI 1.10-1.19), recently (OR 1.13, 95\% CI 1.10-1.18), or recurrently (OR 1.10, 95\% CI 1.05-1.15) before being infected with COVID-19 had a higher risk of presenting at least one of the outcomes than did unvaccinated individuals. When we excluded people living in long-term care facilities, the results were similar. Conclusions: We could not establish a protective role of the immunity conferred by the influenza vaccine on the outcomes of COVID-19 infection, as the risk of COVID-19 complications was higher in vaccinated than in unvaccinated individuals. Our results correspond to the first wave of the COVID-19 pandemic, where more complications and mortalities due to COVID-19 had occurred. Despite that, our study adds more evidence for the analysis of a possible link between the quality of immunity and COVID-19 outcomes, particularly in the PHC setting. ", doi="10.2196/36712", url="https://publichealth.jmir.org/2022/11/e36712", url="http://www.ncbi.nlm.nih.gov/pubmed/36265160" } @Article{info:doi/10.2196/36211, author="Ganser, Iris and Thi{\'e}baut, Rodolphe and Buckeridge, L. David", title="Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis", journal="JMIR Public Health Surveill", year="2022", month="Oct", day="31", volume="8", number="10", pages="e36211", keywords="event-based surveillance", keywords="digital disease detection", keywords="public health surveillance", keywords="influenza", keywords="infectious disease outbreak", keywords="surveillance", keywords="disease", keywords="outbreak", keywords="analysis", keywords="public health", keywords="data", keywords="detection", keywords="detect", keywords="epidemic", abstract="Background: Robust and flexible infectious disease surveillance is crucial for public health. Event-based surveillance (EBS) was developed to allow timely detection of infectious disease outbreaks by using mostly web-based data. Despite its widespread use, EBS has not been evaluated systematically on a global scale in terms of outbreak detection performance. Objective: The aim of this study was to assess the variation in the timing and frequency of EBS reports compared to true outbreaks and to identify the determinants of variability by using the example of seasonal influenza epidemic in 24 countries. Methods: We obtained influenza-related reports between January 2013 and December 2019 from 2 EBS systems, that is, HealthMap and the World Health Organization Epidemic Intelligence from Open Sources (EIOS), and weekly virological influenza counts for the same period from FluNet as the gold standard. Influenza epidemic periods were detected based on report frequency by using Bayesian change point analysis. Timely sensitivity, that is, outbreak detection within the first 2 weeks before or after an outbreak onset was calculated along with sensitivity, specificity, positive predictive value, and timeliness of detection. Linear regressions were performed to assess the influence of country-specific factors on EBS performance. Results: Overall, while monitoring the frequency of EBS reports over 7 years in 24 countries, we detected 175 out of 238 outbreaks (73.5\%) but only 22 out of 238 (9.2\%) within 2 weeks before or after an outbreak onset; in the best case, while monitoring the frequency of health-related reports, we identified 2 out of 6 outbreaks (33\%) within 2 weeks of onset. The positive predictive value varied between 9\% and 100\% for HealthMap and from 0 to 100\% for EIOS, and timeliness of detection ranged from 13\% to 94\% for HealthMap and from 0\% to 92\% for EIOS, whereas system specificity was generally high (59\%-100\%). The number of EBS reports available within a country, the human development index, and the country's geographical location partially explained the high variability in system performance across countries. Conclusions: We documented the global variation of EBS performance and demonstrated that monitoring the report frequency alone in EBS may be insufficient for the timely detection of outbreaks. In particular, in low- and middle-income countries, low data quality and report frequency impair the sensitivity and timeliness of disease surveillance through EBS. Therefore, advances in the development and evaluation and EBS are needed, particularly in low-resource settings. ", doi="10.2196/36211", url="https://publichealth.jmir.org/2022/10/e36211", url="http://www.ncbi.nlm.nih.gov/pubmed/36315218" } @Article{info:doi/10.2196/37177, author="Yang, Zhen and Jiang, Chenghua", title="Pilot Influenza Syndromic Surveillance System Based on Absenteeism and Temperature in China: Development and Usability Study", journal="JMIR Public Health Surveill", year="2022", month="Oct", day="14", volume="8", number="10", pages="e37177", keywords="influenza", keywords="syndromic surveillance system", keywords="face recognition", keywords="infrared thermometer", keywords="absenteeism", keywords="temperature", abstract="Background: Shortcomings of the current school-based infectious disease syndromic surveillance system (SSS) in China include relying on school physicians to collect data manually and ignoring the health information of students in attendance. Objective: This study aimed to design and implement an influenza SSS based on the absenteeism (collected by face recognition) and temperature of attending students (measured by thermal imaging). Methods: An SSS was implemented by extending the functionality of an existing application. The system was implemented in 2 primary schools and 1 junior high school in the Yangtze River Delta, with a total of 3535 students. The examination period was from March 1, 2021, to January 14, 2022, with 174 effective days. The daily and weekly absenteeism and fever rates reported by the system (DAR1 and DFR; WAR1 and WFR) were calculated. The daily and weekly absenteeism rates reported by school physicians (DAR2 and WAR2) and the weekly positive rate of influenza virus (WPRIV, released by the Chinese National Influenza Center) were used as standards to evaluate the quality of the data reported by the system. Results: Absenteeism reported by school physicians (completeness 86.7\%) was 36.5\% of that reported by this system (completeness 100\%), and a significant positive correlation between them was detected (r=0.372, P=.002). When the influenza activity level was moderate, DAR1s were significantly positively correlated among schools (rab=0.508, P=.004; rbc=0.427, P=.02; rac=0.447, P=.01). During the influenza breakout, the gap of DAR1s widened. WAR1 peaked 2 weeks earlier in schools A and B than in school C. Variables significantly positively correlated with the WPRIV were the WAR1 and WAR2 of school A, WAR1 of school C, and WFR of school B. The correlation between the WAR1 and WPRIV was greater than that between the WAR2 and WPRIV in school A. Addition of the WFR to the WAR1 of school B increased the correlation between the WAR1 and WPRIV. Conclusions: Data demonstrated that absenteeism calculation based on face recognition was reliable, but the accuracy of the temperature recorded by the infrared thermometer should be enhanced. Compared with similar SSSs, this system has superior simplicity, cost-effectiveness, data quality, sensitivity, and timeliness. ", doi="10.2196/37177", url="https://publichealth.jmir.org/2022/10/e37177", url="http://www.ncbi.nlm.nih.gov/pubmed/36239991" } @Article{info:doi/10.2196/38551, author="McNeil, Carrie and Verlander, Sarah and Divi, Nomita and Smolinski, Mark", title="The Landscape of Participatory Surveillance Systems Across the One Health Spectrum: Systematic Review", journal="JMIR Public Health Surveill", year="2022", month="Aug", day="5", volume="8", number="8", pages="e38551", keywords="participatory surveillance", keywords="One Health", keywords="citizen science", keywords="community-based surveillance", keywords="infectious disease", keywords="digital disease detection", keywords="community participation", keywords="mobile phone", abstract="Background: Participatory surveillance systems augment traditional surveillance systems through bidirectional community engagement. The digital platform evolution has enabled the expansion of participatory surveillance systems, globally, for the detection of health events impacting people, animals, plants, and the environment, in other words, across the entire One Health spectrum. Objective: The aim of this landscape was to identify and provide descriptive information regarding system focus, geography, users, technology, information shared, and perceived impact of ongoing participatory surveillance systems across the One Health spectrum. Methods: This landscape began with a systematic literature review to identify potential ongoing participatory surveillance systems. A survey was sent to collect standardized data from the contacts of systems identified in the literature review and through direct outreach to stakeholders, experts, and professional organizations. Descriptive analyses of survey and literature review results were conducted across the programs. Results: The landscape identified 60 ongoing single-sector and multisector participatory surveillance systems spanning five continents. Of these, 29 (48\%) include data on human health, 26 (43\%) include data on environmental health, and 24 (40\%) include data on animal health. In total, 16 (27\%) systems are multisectoral; of these, 9 (56\%) collect animal and environmental health data; 3 (19\%) collect human, animal, and environmental health data; 2 (13\%) collect human and environmental health data; and 2 (13\%) collect human and animal health data. Out of 60 systems, 31 (52\%) are designed to cover a national scale, compared to those with a subnational (n=19, 32\%) or multinational (n=10, 17\%) focus. All systems use some form of digital technology. Email communication or websites (n=40, 67\%) and smartphones (n=29, 48\%) are the most common technologies used, with some using both. Systems have capabilities to download geolocation data (n=31, 52\%), photographs (n=29, 48\%), and videos (n=6, 10\%), and can incorporate lab data or sample collection (n=15, 25\%). In sharing information back with users, most use visualization, such as maps (n=43, 72\%); training and educational materials (n=37, 62\%); newsletters, blogs, and emails (n=34, 57\%); and disease prevention information (n=32, 53\%). Out of the 46 systems responding to the survey regarding perceived impacts of their systems, 36 (78\%) noted ``improved community knowledge and understanding'' and 31 (67\%) noted ``earlier detection.'' Conclusions: The landscape demonstrated the breadth of applicability of participatory surveillance around the world to collect data from community members and trained volunteers in order to inform the detection of events, from invasive plant pests to weekly influenza symptoms. Acknowledging the importance of bidirectionality of information, these systems simultaneously share findings back with the users. Such directly engaged community detection systems capture events early and provide opportunities to stop outbreaks quickly. ", doi="10.2196/38551", url="https://publichealth.jmir.org/2022/8/e38551", url="http://www.ncbi.nlm.nih.gov/pubmed/35930345" } @Article{info:doi/10.2196/25803, author="de Lusignan, Simon and Tsang, M. Ruby S. and Akinyemi, Oluwafunmi and Lopez Bernal, Jamie and Amirthalingam, Gayatri and Sherlock, Julian and Smith, Gillian and Zambon, Maria and Howsam, Gary and Joy, Mark", title="Adverse Events of Interest Following Influenza Vaccination in the First Season of Adjuvanted Trivalent Immunization: Retrospective Cohort Study", journal="JMIR Public Health Surveill", year="2022", month="Mar", day="28", volume="8", number="3", pages="e25803", keywords="influenza", keywords="influenza vaccines", keywords="adverse events of interest", keywords="computerized medical record systems", keywords="sentinel surveillance", abstract="Background: Vaccination is the most effective form of prevention of seasonal influenza; the United Kingdom has a national influenza vaccination program to cover targeted population groups. Influenza vaccines are known to be associated with some common minor adverse events of interest (AEIs), but it is not known if the adjuvanted trivalent influenza vaccine (aTIV), first offered in the 2018/2019 season, would be associated with more AEIs than other types of vaccines. Objective: We aim to compare the incidence of AEIs associated with different types of seasonal influenza vaccines offered in the 2018/2019 season. Methods: We carried out a retrospective cohort study using computerized medical record data from the Royal College of General Practitioners Research and Surveillance Centre sentinel network database. We extracted data on vaccine exposure and consultations for European Medicines Agency--specified AEIs for the 2018/2019 influenza season. We used a self-controlled case series design; computed relative incidence (RI) of AEIs following vaccination; and compared the incidence of AEIs associated with aTIV, the quadrivalent influenza vaccine, and the live attenuated influenza vaccine. We also compared the incidence of AEIs for vaccinations that took place in a practice with those that took place elsewhere. Results: A total of 1,024,160 individuals received a seasonal influenza vaccine, of which 165,723 individuals reported a total of 283,355 compatible symptoms in the 2018/2019 season. Most AEIs occurred within 7 days following vaccination, with a seasonal effect observed. Using aTIV as the reference group, the quadrivalent influenza vaccine was associated with a higher incidence of AEIs (RI 1.46, 95\% CI 1.41-1.52), whereas the live attenuated influenza vaccine was associated with a lower incidence of AEIs (RI 0.79, 95\% CI 0.73-0.83). No effect of vaccination setting on the incidence of AEIs was observed. Conclusions: Routine sentinel network data offer an opportunity to make comparisons between safety profiles of different vaccines. Evidence that supports the safety of newer types of vaccines may be reassuring for patients and could help improve uptake in the future. ", doi="10.2196/25803", url="https://publichealth.jmir.org/2022/3/e25803", url="http://www.ncbi.nlm.nih.gov/pubmed/35343907" } @Article{info:doi/10.2196/25658, author="Huang, Yun and Luo, Chongliang and Jiang, Ying and Du, Jingcheng and Tao, Cui and Chen, Yong and Hao, Yuantao", title="A Bayesian Network to Predict the Risk of Post Influenza Vaccination Guillain-Barr{\'e} Syndrome: Development and Validation Study", journal="JMIR Public Health Surveill", year="2022", month="Mar", day="25", volume="8", number="3", pages="e25658", keywords="adverse events", keywords="Bayesian network", keywords="Guillain-Barr{\'e} syndrome", keywords="risk prediction", keywords="trivalent influenza vaccine", abstract="Background: Identifying the key factors of Guillain-Barr{\'e} syndrome (GBS) and predicting its occurrence are vital for improving the prognosis of patients with GBS. However, there are scarcely any publications on a forewarning model of GBS. A Bayesian network (BN) model, which is known to be an accurate, interpretable, and interaction-sensitive graph model in many similar domains, is worth trying in GBS risk prediction. Objective: The aim of this study is to determine the most significant factors of GBS and further develop and validate a BN model for predicting GBS risk. Methods: Large-scale influenza vaccine postmarketing surveillance data, including 79,165 US (obtained from the Vaccine Adverse Event Reporting System between 1990 and 2017) and 12,495 European (obtained from the EudraVigilance system between 2003 and 2016) adverse events (AEs) reports, were extracted for model development and validation. GBS, age, gender, and the top 50 prevalent AEs were included for initial BN construction using the R package bnlearn. Results: Age, gender, and 10 AEs were identified as the most significant factors of GBS. The posttest probability of GBS suggested that male vaccinees aged 50-64 years and without erythema should be on the alert or be warned by clinicians about an increased risk of GBS, especially when they also experience symptoms of asthenia, hypesthesia, muscular weakness, or paresthesia. The established BN model achieved an area under the receiver operating characteristic curve of 0.866 (95\% CI 0.865-0.867), sensitivity of 0.752 (95\% CI 0.749-0.756), specificity of 0.882 (95\% CI 0.879-0.885), and accuracy of 0.882 (95\% CI 0.879-0.884) for predicting GBS risk during the internal validation and obtained values of 0.829, 0.673, 0.854, and 0.843 for area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy, respectively, during the external validation. Conclusions: The findings of this study illustrated that a BN model can effectively identify the most significant factors of GBS, improve understanding of the complex interactions among different postvaccination symptoms through its graphical representation, and accurately predict the risk of GBS. The established BN model could further assist clinical decision-making by providing an estimated risk of GBS for a specific vaccinee or be developed into an open-access platform for vaccinees' self-monitoring. ", doi="10.2196/25658", url="https://publichealth.jmir.org/2022/3/e25658", url="http://www.ncbi.nlm.nih.gov/pubmed/35333192" } @Article{info:doi/10.2196/25532, author="Morel, Benoit and Bouleux, Guillaume and Viallon, Alain and Maignan, Maxime and Provoost, Luc and Bernadac, Jean-Christophe and Devidal, Sarah and Pillet, Sylvie and Cantais, Aymeric and Mory, Olivier", title="Evaluating the Increased Burden of Cardiorespiratory Illness Visits to Adult Emergency Departments During Flu and Bronchiolitis Outbreaks in the Pediatric Population: Retrospective Multicentric Time Series Analysis", journal="JMIR Public Health Surveill", year="2022", month="Mar", day="10", volume="8", number="3", pages="e25532", keywords="respiratory infections", keywords="emergency departments", keywords="flu outbreak", keywords="bronchiolitis outbreak", keywords="cardiorespiratory illness", keywords="time series analysis", keywords="influenza", keywords="bronchiolitis", keywords="outbreak", keywords="pediatrics", abstract="Background: Cardiorespiratory decompensation (CRD) visits have a profound effect on adult emergency departments (EDs). Respiratory pathogens like respiratory syncytial virus (RSV) and influenza virus are common reasons for increased activity in pediatric EDs and are associated with CRD in the adult population. Given the seasonal aspects of such challenging pathology, it would be advantageous to predict their variations. Objective: The goal of this study was to evaluate the increased burden of CRD in adult EDs during flu and bronchiolitis outbreaks in the pediatric population. Methods: An ecological study was conducted, based on admissions to the adult ED of the Centre Hospitalier Universitaire (CHU) of Grenoble and Saint Etienne from June 29, 2015 to March 22, 2020. The outbreak periods for bronchiolitis and flu in the pediatric population were defined with a decision-making support tool, PREDAFLU, used in the pediatric ED. A Kruskal-Wallis variance analysis and a Spearman monotone dependency were performed in order to study the relationship between the number of adult ED admissions for the International Classification of Diseases (ICD)-10 codes related to cardiorespiratory diagnoses and the presence of an epidemic outbreak as defined with PREDAFLU. Results: The increase in visits to the adult ED for CRD and the bronchiolitis and flu outbreaks had a similar distribution pattern (CHU Saint Etienne: $\chi$23=102.7, P<.001; CHU Grenoble: $\chi$23=126.67, P<.001) and were quite dependent in both hospital settings (CHU Saint Etienne: Spearman $\rho$=0.64; CHU Grenoble: Spearman $\rho$=0.71). The increase in ED occupancy for these pathologies was also significantly related to the pediatric respiratory infection outbreaks. These 2 criteria gave an idea of the increased workload in the ED due to CRD during the bronchiolitis and flu outbreaks in the pediatric population. Conclusions: This study established that CRD visits and bed occupancy for adult EDs were significantly increased during bronchiolitis and pediatric influenza outbreaks. Therefore, a prediction tool for these outbreaks such as PREDAFLU can be used to provide early warnings of increased activity in adult EDs for CRD visits. ", doi="10.2196/25532", url="https://publichealth.jmir.org/2022/3/e25532", url="http://www.ncbi.nlm.nih.gov/pubmed/35266876" } @Article{info:doi/10.2196/32364, author="Cai, Owen and Sousa-Pinto, Bernardo", title="United States Influenza Search Patterns Since the Emergence of COVID-19: Infodemiology Study", journal="JMIR Public Health Surveill", year="2022", month="Mar", day="3", volume="8", number="3", pages="e32364", keywords="COVID-19", keywords="influenza", keywords="surveillance", keywords="media coverage", keywords="Google Trends", keywords="infodemiology", keywords="monitoring", keywords="trend", keywords="United States", keywords="information-seeking", keywords="online health information", abstract="Background: The emergence and media coverage of COVID-19 may have affected influenza search patterns, possibly affecting influenza surveillance results using Google Trends. Objective: We aimed to investigate if the emergence of COVID-19 was associated with modifications in influenza search patterns in the United States. Methods: We retrieved US Google Trends data (relative number of searches for specified terms) for the topics influenza, Coronavirus disease 2019, and symptoms shared between influenza and COVID-19. We calculated the correlations between influenza and COVID-19 search data for a 1-year period after the first COVID-19 diagnosis in the United States (January 21, 2020 to January 20, 2021). We constructed a seasonal autoregressive integrated moving average model and compared predicted search volumes, using the 4 previous years, with Google Trends relative search volume data. We built a similar model for shared symptoms data. We also assessed correlations for the past 5 years between Google Trends influenza data, US Centers for Diseases Control and Prevention influenza-like illness data, and influenza media coverage data. Results: We observed a nonsignificant weak correlation ($\rho$= --0.171; P=0.23) between COVID-19 and influenza Google Trends data. Influenza search volumes for 2020-2021 distinctly deviated from values predicted by seasonal autoregressive integrated moving average models---for 6 weeks within the first 13 weeks after the first COVID-19 infection was confirmed in the United States, the observed volume of searches was higher than the upper bound of 95\% confidence intervals for predicted values. Similar results were observed for shared symptoms with influenza and COVID-19 data. The correlation between Google Trends influenza data and CDC influenza-like-illness data decreased after the emergence of COVID-19 (2020-2021: $\rho$=0.643; 2019-2020: $\rho$=0.902), while the correlation between Google Trends influenza data and influenza media coverage volume remained stable (2020-2021: $\rho$=0.746; 2019-2020: $\rho$=0.707). Conclusions: Relevant differences were observed between predicted and observed influenza Google Trends data the year after the onset of the COVID-19 pandemic in the United States. Such differences are possibly due to media coverage, suggesting limitations to the use of Google Trends as a flu surveillance tool. ", doi="10.2196/32364", url="https://publichealth.jmir.org/2022/3/e32364", url="http://www.ncbi.nlm.nih.gov/pubmed/34878996" } @Article{info:doi/10.2196/28268, author="Geyer, E. Rachel and Kotnik, Henry Jack and Lyon, Victoria and Brandstetter, Elisabeth and Zigman Suchsland, Monica and Han, D. Peter and Graham, Chelsey and Ilcisin, Misja and Kim, E. Ashley and Chu, Y. Helen and Nickerson, A. Deborah and Starita, M. Lea and Bedford, Trevor and Lutz, Barry and Thompson, J. Matthew", title="Diagnostic Accuracy of an At-Home, Rapid Self-test for Influenza: Prospective Comparative Accuracy Study", journal="JMIR Public Health Surveill", year="2022", month="Feb", day="22", volume="8", number="2", pages="e28268", keywords="influenza", keywords="rapid testing", keywords="acute respiratory illness", keywords="self-collection", keywords="self-testing", keywords="mHealth", keywords="mobile health", keywords="home collection", keywords="home testing", keywords="mobile phone", abstract="Background: Rapid diagnostic tests (RDTs) for influenza used by individuals at home could potentially expand access to testing and reduce the impact of influenza on health systems. Improving access to testing could lead to earlier diagnosis following symptom onset, allowing more rapid interventions for those who test positive, including behavioral changes to minimize spread. However, the accuracy of RDTs for influenza has not been determined in self-testing populations. Objective: This study aims to assess the accuracy of an influenza RDT conducted at home by lay users with acute respiratory illness compared with that of a self-collected sample by the same individual mailed to a laboratory for reference testing. Methods: We conducted a comparative accuracy study of an at-home influenza RDT (Ellume) in a convenience sample of individuals experiencing acute respiratory illness symptoms. Participants were enrolled in February and March 2020 from the Greater Seattle region in Washington, United States. Participants were mailed the influenza RDT and reference sample collection materials, which they completed and returned for quantitative reverse-transcription polymerase chain reaction influenza testing in a central laboratory. We explored the impact of age, influenza type, duration, and severity of symptoms on RDT accuracy and on cycle threshold for influenza virus and ribonuclease P, a marker of human DNA. Results: A total of 605 participants completed all study steps and were included in our analysis, of whom 87 (14.4\%) tested positive for influenza by quantitative reverse-transcription polymerase chain reaction (70/87, 80\% for influenza A and 17/87, 20\% for influenza B). The overall sensitivity and specificity of the RDT compared with the reference test were 61\% (95\% CI 50\%-71\%) and 95\% (95\% CI 93\%-97\%), respectively. Among individuals with symptom onset ?72 hours, sensitivity was 63\% (95\% CI 48\%-76\%) and specificity was 94\% (95\% CI 91\%-97\%), whereas, for those with duration >72 hours, sensitivity and specificity were 58\% (95\% CI 41\%-74\%) and 96\% (95\% CI 93\%-98\%), respectively. Viral load on reference swabs was negatively correlated with symptom onset, and quantities of the endogenous marker gene ribonuclease P did not differ among reference standard positive and negative groups, age groups, or influenza subtypes. The RDT did not have higher sensitivity or specificity among those who reported more severe illnesses. Conclusions: The sensitivity and specificity of the self-test were comparable with those of influenza RDTs used in clinical settings. False-negative self-test results were more common when the test was used after 72 hours of symptom onset but were not related to inadequate swab collection or severity of illness. Therefore, the deployment of home tests may provide a valuable tool to support the management of influenza and other respiratory infections. ", doi="10.2196/28268", url="https://publichealth.jmir.org/2022/2/e28268", url="http://www.ncbi.nlm.nih.gov/pubmed/35191852" } @Article{info:doi/10.2196/31131, author="Katayama, Yusuke and Kiyohara, Kosuke and Hirose, Tomoya and Ishida, Kenichiro and Tachino, Jotaro and Nakao, Shunichiro and Noda, Tomohiro and Ojima, Masahiro and Kiguchi, Takeyuki and Matsuyama, Tasuku and Kitamura, Tetsuhisa", title="An Association of Influenza Epidemics in Children With Mobile App Data: Population-Based Observational Study in Osaka, Japan", journal="JMIR Form Res", year="2022", month="Feb", day="10", volume="6", number="2", pages="e31131", keywords="syndromic surveillance", keywords="mobile app", keywords="influenza", keywords="epidemic", keywords="children", abstract="Background: Early surveillance to prevent the spread of influenza is a major public health concern. If there is an association of influenza epidemics with mobile app data, it may be possible to forecast influenza earlier and more easily. Objective: We aimed to assess the relationship between seasonal influenza and the frequency of mobile app use among children in Osaka Prefecture, Japan. Methods: This was a retrospective observational study that was performed over a three-year period from January 2017 to December 2019. Using a linear regression model, we calculated the R2 value of the regression model to evaluate the relationship between the number of ``fever'' events selected in the mobile app and the number of influenza patients ?14 years of age. We conducted three-fold cross-validation using data from two years as the training data set and the data of the remaining year as the test data set to evaluate the validity of the regression model. And we calculated Spearman correlation coefficients between the calculated number of influenza patients estimated using the regression model and the number of influenza patients, limited to the period from December to April when influenza is prevalent in Japan. Results: We included 29,392 mobile app users. The R2 value for the linear regression model was 0.944, and the adjusted R2 value was 0.915. The mean Spearman correlation coefficient for the three regression models was 0.804. During the influenza season (December--April), the Spearman correlation coefficient between the number of influenza patients and the calculated number estimated using the linear regression model was 0.946 (P<.001). Conclusions: In this study, the number of times that mobile apps were used was positively associated with the number of influenza patients. In particular, there was a good association of the number of influenza patients with the number of ``fever'' events selected in the mobile app during the influenza epidemic season. ", doi="10.2196/31131", url="https://formative.jmir.org/2022/2/e31131", url="http://www.ncbi.nlm.nih.gov/pubmed/35142628" } @Article{info:doi/10.2196/26523, author="Cawley, Caoimhe and Bergey, Fran{\c{c}}ois and Mehl, Alicia and Finckh, Ashlee and Gilsdorf, Andreas", title="Novel Methods in the Surveillance of Influenza-Like Illness in Germany Using Data From a Symptom Assessment App (Ada): Observational Case Study", journal="JMIR Public Health Surveill", year="2021", month="Nov", day="4", volume="7", number="11", pages="e26523", keywords="ILI", keywords="influenza", keywords="syndromic surveillance", keywords="participatory surveillance", keywords="digital surveillance", keywords="mobile phone", abstract="Background: Participatory epidemiology is an emerging field harnessing consumer data entries of symptoms. The free app Ada allows users to enter the symptoms they are experiencing and applies a probabilistic reasoning model to provide a list of possible causes for these symptoms. Objective: The objective of our study is to explore the potential contribution of Ada data to syndromic surveillance by comparing symptoms of influenza-like illness (ILI) entered by Ada users in Germany with data from a national population-based reporting system called GrippeWeb. Methods: We extracted data for all assessments performed by Ada users in Germany over 3 seasons (2017/18, 2018/19, and 2019/20) and identified those with ILI (report of fever with cough or sore throat). The weekly proportion of assessments in which ILI was reported was calculated (overall and stratified by age group), standardized for the German population, and compared with trends in ILI rates reported by GrippeWeb using time series graphs, scatterplots, and Pearson correlation coefficient. Results: In total, 2.1 million Ada assessments (for any symptoms) were included. Within seasons and across age groups, the Ada data broadly replicated trends in estimated weekly ILI rates when compared with GrippeWeb data (Pearson correlation---2017-18: r=0.86, 95\% CI 0.76-0.92; P<.001; 2018-19: r=0.90, 95\% CI 0.84-0.94; P<.001; 2019-20: r=0.64, 95\% CI 0.44-0.78; P<.001). However, there were differences in the exact timing and nature of the epidemic curves between years. Conclusions: With careful interpretation, Ada data could contribute to identifying broad ILI trends in countries without existing population-based monitoring systems or to the syndromic surveillance of symptoms not covered by existing systems. ", doi="10.2196/26523", url="https://publichealth.jmir.org/2021/11/e26523", url="http://www.ncbi.nlm.nih.gov/pubmed/34734836" } @Article{info:doi/10.2196/31983, author="Benis, Arriel and Chatsubi, Anat and Levner, Eugene and Ashkenazi, Shai", title="Change in Threads on Twitter Regarding Influenza, Vaccines, and Vaccination During the COVID-19 Pandemic: Artificial Intelligence--Based Infodemiology Study", journal="JMIR Infodemiology", year="2021", month="Oct", day="14", volume="1", number="1", pages="e31983", keywords="influenza", keywords="vaccines", keywords="vaccination", keywords="social media", keywords="social networks", keywords="health communication", keywords="artificial intelligence", keywords="machine learning", keywords="text mining", keywords="infodemiology", keywords="COVID-19", keywords="SARS-CoV-2", abstract="Background: Discussions of health issues on social media are a crucial information source reflecting real-world responses regarding events and opinions. They are often important in public health care, since these are influencing pathways that affect vaccination decision-making by hesitant individuals. Artificial intelligence methodologies based on internet search engine queries have been suggested to detect disease outbreaks and population behavior. Among social media, Twitter is a common platform of choice to search and share opinions and (mis)information about health care issues, including vaccination and vaccines. Objective: Our primary objective was to support the design and implementation of future eHealth strategies and interventions on social media to increase the quality of targeted communication campaigns and therefore increase influenza vaccination rates. Our goal was to define an artificial intelligence--based approach to elucidate how threads in Twitter on influenza vaccination changed during the COVID-19 pandemic. Such findings may support adapted vaccination campaigns and could be generalized to other health-related mass communications. Methods: The study comprised the following 5 stages: (1) collecting tweets from Twitter related to influenza, vaccines, and vaccination in the United States; (2) data cleansing and storage using machine learning techniques; (3) identifying terms, hashtags, and topics related to influenza, vaccines, and vaccination; (4) building a dynamic folksonomy of the previously defined vocabulary (terms and topics) to support the understanding of its trends; and (5) labeling and evaluating the folksonomy. Results: We collected and analyzed 2,782,720 tweets of 420,617 unique users between December 30, 2019, and April 30, 2021. These tweets were in English, were from the United States, and included at least one of the following terms: ``flu,'' ``influenza,'' ``vaccination,'' ``vaccine,'' and ``vaxx.'' We noticed that the prevalence of the terms vaccine and vaccination increased over 2020, and that ``flu'' and ``covid'' occurrences were inversely correlated as ``flu'' disappeared over time from the tweets. By combining word embedding and clustering, we then identified a folksonomy built around the following 3 topics dominating the content of the collected tweets: ``health and medicine (biological and clinical aspects),'' ``protection and responsibility,'' and ``politics.'' By analyzing terms frequently appearing together, we noticed that the tweets were related mainly to COVID-19 pandemic events. Conclusions: This study focused initially on vaccination against influenza and moved to vaccination against COVID-19. Infoveillance supported by machine learning on Twitter and other social media about topics related to vaccines and vaccination against communicable diseases and their trends can lead to the design of personalized messages encouraging targeted subpopulations' engagement in vaccination. A greater likelihood that a targeted population receives a personalized message is associated with higher response, engagement, and proactiveness of the target population for the vaccination process. ", doi="10.2196/31983", url="https://infodemiology.jmir.org/2021/1/e31983", url="http://www.ncbi.nlm.nih.gov/pubmed/34693212" } @Article{info:doi/10.2196/26869, author="Dharanikota, Spurthy and LeRouge, M. Cynthia and Lyon, Victoria and Durneva, Polina and Thompson, Matthew", title="Identifying Enablers of Participant Engagement in Clinical Trials of Consumer Health Technologies: Qualitative Study of Influenza Home Testing", journal="J Med Internet Res", year="2021", month="Sep", day="14", volume="23", number="9", pages="e26869", keywords="consumer health care technologies", keywords="CHTs", keywords="smartphone-supported home tests", keywords="Smart-HT", keywords="premarket clinical trials", keywords="trial engagement", keywords="at-home diagnostic testing", keywords="mobile phone", abstract="Background: A rise in the recent trend of self-managing health using consumer health technologies highlights the importance of efficient and successful consumer health technology trials. Trials are particularly essential to support large-scale implementations of consumer health technologies, such as smartphone-supported home tests. However, trials are generally fraught with challenges, such as inadequate enrollment, lack of fidelity to interventions, and high dropout rates. Understanding the reasons underlying individuals' participation in trials can inform the design and execution of future trials of smartphone-supported home tests. Objective: This study aims to identify the enablers of potential participants' trial engagement for clinical trials of smartphone-supported home tests. We use influenza home testing as our instantiation of a consumer health technology subject to trial to investigate the dispositional and situational enablers that influenced trial engagement. Methods: We conducted semistructured interviews with 31 trial participants using purposive sampling to facilitate demographic diversity. The interviews included a discussion of participants' personal characteristics and external factors that enabled their trial engagement with a smartphone-supported home test for influenza. We performed both deductive and inductive thematic analyses to analyze the interview transcripts and identify enabler themes. Results: Our thematic analyses revealed a structure of dispositional and situational enablers that enhanced trial engagement. Situationally, clinical affiliation, personal advice, promotional recruitment strategies, financial incentives, and insurance status influenced trial engagement. In addition, digital health literacy, motivation to advance medical research, personal innovativeness, altruism, curiosity, positive attitude, and potential to minimize doctors' visits were identified as the dispositional enablers for trial engagement in our study. Conclusions: We organized the identified themes for dispositional and situational enablers of trial engagement with a smartphone-supported home test into a research framework that can guide future research as well as the trial design and execution of smartphone-supported home tests. We suggest several trial design and engagement strategies to enhance the financial and scientific viability of these trials that pave the way for advancements in patient care. Furthermore, our study also offers practical strategies to trial organizers to enhance participants' enrollment and engagement in clinical trials of these home tests. ", doi="10.2196/26869", url="https://www.jmir.org/2021/9/e26869", url="http://www.ncbi.nlm.nih.gov/pubmed/34519664" } @Article{info:doi/10.2196/28116, author="Greshake Tzovaras, Bastian and Senabre Hidalgo, Enric and Alexiou, Karolina and Baldy, Lukaz and Morane, Basile and Bussod, Ilona and Fribourg, Melvin and Wac, Katarzyna and Wolf, Gary and Ball, Mad", title="Using an Individual-Centered Approach to Gain Insights From Wearable Data in the Quantified Flu Platform: Netnography Study", journal="J Med Internet Res", year="2021", month="Sep", day="10", volume="23", number="9", pages="e28116", keywords="symptom tracking", keywords="COVID-19", keywords="wearable devices", keywords="self-tracking", keywords="citizen science", keywords="netnographic analysis", keywords="cocreation", abstract="Background: Wearables have been used widely for monitoring health in general, and recent research results show that they can be used to predict infections based on physiological symptoms. To date, evidence has been generated in large, population-based settings. In contrast, the Quantified Self and Personal Science communities are composed of people who are interested in learning about themselves individually by using their own data, which are often gathered via wearable devices. Objective: This study aims to explore how a cocreation process involving a heterogeneous community of personal science practitioners can develop a collective self-tracking system for monitoring symptoms of infection alongside wearable sensor data. Methods: We engaged in a cocreation and design process with an existing community of personal science practitioners to jointly develop a working prototype of a web-based tool for symptom tracking. In addition to the iterative creation of the prototype (started on March 16, 2020), we performed a netnographic analysis to investigate the process of how this prototype was created in a decentralized and iterative fashion. Results: The Quantified Flu prototype allowed users to perform daily symptom reporting and was capable of presenting symptom reports on a timeline together with resting heart rates, body temperature data, and respiratory rates measured by wearable devices. We observed a high level of engagement; over half of the users (52/92, 56\%) who engaged in symptom tracking became regular users and reported over 3 months of data each. Furthermore, our netnographic analysis highlighted how the current Quantified Flu prototype was a result of an iterative and continuous cocreation process in which new prototype releases sparked further discussions of features and vice versa. Conclusions: As shown by the high level of user engagement and iterative development process, an open cocreation process can be successfully used to develop a tool that is tailored to individual needs, thereby decreasing dropout rates. ", doi="10.2196/28116", url="https://www.jmir.org/2021/9/e28116", url="http://www.ncbi.nlm.nih.gov/pubmed/34505836" } @Article{info:doi/10.2196/23305, author="Jang, Beakcheol and Kim, Inhwan and Kim, Wook Jong", title="Effective Training Data Extraction Method to Improve Influenza Outbreak Prediction from Online News Articles: Deep Learning Model Study", journal="JMIR Med Inform", year="2021", month="May", day="25", volume="9", number="5", pages="e23305", keywords="influenza", keywords="training data extraction", keywords="keyword", keywords="sorting", keywords="word embedding", keywords="Pearson correlation coefficient", keywords="long short-term memory", keywords="surveillance", keywords="infodemiology", keywords="infoveillance", keywords="model", abstract="Background: Each year, influenza affects 3 to 5 million people and causes 290,000 to 650,000 fatalities worldwide. To reduce the fatalities caused by influenza, several countries have established influenza surveillance systems to collect early warning data. However, proper and timely warnings are hindered by a 1- to 2-week delay between the actual disease outbreaks and the publication of surveillance data. To address the issue, novel methods for influenza surveillance and prediction using real-time internet data (such as search queries, microblogging, and news) have been proposed. Some of the currently popular approaches extract online data and use machine learning to predict influenza occurrences in a classification mode. However, many of these methods extract training data subjectively, and it is difficult to capture the latent characteristics of the data correctly. There is a critical need to devise new approaches that focus on extracting training data by reflecting the latent characteristics of the data. Objective: In this paper, we propose an effective method to extract training data in a manner that reflects the hidden features and improves the performance by filtering and selecting only the keywords related to influenza before the prediction. Methods: Although word embedding provides a distributed representation of words by encoding the hidden relationships between various tokens, we enhanced the word embeddings by selecting keywords related to the influenza outbreak and sorting the extracted keywords using the Pearson correlation coefficient in order to solely keep the tokens with high correlation with the actual influenza outbreak. The keyword extraction process was followed by a predictive model based on long short-term memory that predicts the influenza outbreak. To assess the performance of the proposed predictive model, we used and compared a variety of word embedding techniques. Results: Word embedding without our proposed sorting process showed 0.8705 prediction accuracy when 50.2 keywords were selected on average. Conversely, word embedding using our proposed sorting process showed 0.8868 prediction accuracy and an improvement in prediction accuracy of 12.6\%, although smaller amounts of training data were selected, with only 20.6 keywords on average. Conclusions: The sorting stage empowers the embedding process, which improves the feature extraction process because it acts as a knowledge base for the prediction component. The model outperformed other current approaches that use flat extraction before prediction. ", doi="10.2196/23305", url="https://medinform.jmir.org/2021/5/e23305", url="http://www.ncbi.nlm.nih.gov/pubmed/34032577" } @Article{info:doi/10.2196/27433, author="Fahim, Manal and Ghonim, Sood Hanaa Abu El and Roshdy, H. Wael and Naguib, Amel and Elguindy, Nancy and AbdelFatah, Mohamad and Hassany, Mohamed and Mohsen, Amira and Afifi, Salma and Eid, Alaa", title="Coinfection With SARS-CoV-2 and Influenza A(H1N1) in a Patient Seen at an Influenza-like Illness Surveillance Site in Egypt: Case Report", journal="JMIR Public Health Surveill", year="2021", month="Apr", day="28", volume="7", number="4", pages="e27433", keywords="influenza-like Illness", keywords="pandemic", keywords="SARS-CoV-2", keywords="COVID-19", keywords="influenza", keywords="virus", keywords="case study", keywords="Egypt", keywords="flu", keywords="coinfection", keywords="infectious disease", keywords="surveillance", keywords="outcome", keywords="demographic", abstract="Background: Sentinel surveillance of influenza-like illness (ILI) in Egypt started in 2000 at 8 sentinel sites geographically distributed all over the country. In response to the COVID-19 pandemic, SARS-CoV-2 was added to the panel of viral testing by polymerase chain reaction for the first 2 patients with ILI seen at one of the sentinel sites. We report the first SARS-CoV-2 and influenza A(H1N1) virus co-infection with mild symptoms detected through routine ILI surveillance in Egypt. Objective: This report aims to describe how the case was identified and the demographic and clinical characteristics and outcomes of the patient. Methods: The case was identified by Central Public Health Laboratory staff, who contacted the ILI sentinel surveillance officer at the Ministry of Health. The case patient was contacted through a telephone call. Detailed information about the patient's clinical picture, course of disease, and outcome was obtained. The contacts of the patient were investigated for acute respiratory symptoms, disease confirmation, and outcomes. Results: Among 510 specimens collected from patients with ILI symptoms from October 2019 to August 2020, 61 (12.0\%) were COVID-19--positive and 29 (5.7\%) tested positive for influenza, including 15 (51.7\%) A(H1N1), 11 (38.0\%) A(H3N2), and 3 (10.3\%) influenza B specimens. A 21-year-old woman was confirmed to have SARS-CoV-2 and influenza A(H1N1) virus coinfection. She had a high fever of 40.2 {\textdegree}C and mild respiratory symptoms that resolved within 2 days with symptomatic treatment. All five of her family contacts had mild respiratory symptoms 2-3 days after exposure to the confirmed case, and their symptoms resolved without treatment or investigation. Conclusions: This case highlights the possible occurrence of SARS-CoV-2/influenza A(H1N1) coinfection in younger and healthy people, who may resolve the infection rapidly. We emphasize the usefulness of the surveillance system for detection of viral causative agents of ILI and recommend broadening of the testing panel, especially if it can guide case management. ", doi="10.2196/27433", url="https://publichealth.jmir.org/2021/4/e27433", url="http://www.ncbi.nlm.nih.gov/pubmed/33784634" } @Article{info:doi/10.2196/25977, author="Benis, Arriel and Khodos, Anna and Ran, Sivan and Levner, Eugene and Ashkenazi, Shai", title="Social Media Engagement and Influenza Vaccination During the COVID-19 Pandemic: Cross-sectional Survey Study", journal="J Med Internet Res", year="2021", month="Mar", day="16", volume="23", number="3", pages="e25977", keywords="influenza", keywords="vaccines", keywords="vaccination", keywords="social media", keywords="online social networking", keywords="health literacy", keywords="eHealth", keywords="information dissemination", keywords="access to information", keywords="COVID-19", abstract="Background: Vaccines are one of the most important achievements of modern medicine. However, their acceptance is only partial, with vaccine hesitancy and refusal representing a major health threat. Influenza vaccines have low compliance since repeated, annual vaccination is required. Influenza vaccines stimulate discussions both in the real world and online. Social media is currently a significant source of health and medical information. Elucidating the association between social media engagement and influenza vaccination is important and may be applicable to other vaccines, including ones against COVID-19. Objective: The goal of this study is to characterize profiles of social media engagement regarding the influenza vaccine and their association with knowledge and compliance in order to support improvement of future web-associated vaccination campaigns. Methods: A weblink to an online survey in Hebrew was disseminated over social media and messaging platforms. The survey answers were collected during April 2020. Anonymous and volunteer participants aged 21 years and over answered 30 questions related to sociodemographics; social media usage; influenza- and vaccine-related knowledge and behavior; health-related information searching, its reliability, and its influence; and COVID-19-related information searching. A univariate descriptive data analysis was performed, followed by multivariate analysis via building a decision tree to define the most important attributes associated with vaccination compliance. Results: A total of 213 subjects responded to the survey, of whom 207 were included in the analysis; the majority of the respondents were female, were aged 21 to 40 years, had 1 to 2 children, lived in central Israel, were secular Israeli natives, had higher education, and had a salary close to the national average. Most respondents (128/207, 61.8\%) were not vaccinated against influenza in 2019 and used social media. Participants that used social media were younger, secular, and living in high-density agglomerations and had lower influenza vaccination rates. The perceived influence and reliability of the information on social media about COVID-19 were generally similar to those perceptions about influenza. Conclusions: Using social media is negatively linked to compliance with seasonal influenza vaccination in this study. A high proportion of noncompliant individuals can lead to increased consumption of health care services and can, therefore, overload these health services. This is particularly crucial with a concomitant outbreak, such as COVID-19. Health care professionals should use improved and targeted health communication campaigns with the aid of experts in social media. Targeted communication, based on sociodemographic factors and personalized social media usage, might increase influenza vaccination rates and compliance with other vaccines as well. ", doi="10.2196/25977", url="https://www.jmir.org/2021/3/e25977", url="http://www.ncbi.nlm.nih.gov/pubmed/33651709" } @Article{info:doi/10.2196/24696, author="Baral, David Stefan and Rucinski, Blair Katherine and Twahirwa Rwema, Olivier Jean and Rao, Amrita and Prata Menezes, Neia and Diouf, Daouda and Kamarulzaman, Adeeba and Phaswana-Mafuya, Nancy and Mishra, Sharmistha", title="The Relationship Between the Global Burden of Influenza From 2017 to 2019 and COVID-19: Descriptive Epidemiological Assessment", journal="JMIR Public Health Surveill", year="2021", month="Mar", day="2", volume="7", number="3", pages="e24696", keywords="SARS-CoV-2", keywords="COVID-19", keywords="influenza", keywords="descriptive epidemiology", keywords="epidemiology", keywords="assessment", keywords="relationship", keywords="flu", keywords="virus", keywords="burden", keywords="global health", keywords="public health", keywords="transmission", keywords="pattern", abstract="Background: SARS-CoV-2 and influenza are lipid-enveloped viruses with differential morbidity and mortality but shared modes of transmission. Objective: With a descriptive epidemiological framing, we assessed whether recent historical patterns of regional influenza burden are reflected in the observed heterogeneity in COVID-19 cases across regions of the world. Methods: Weekly surveillance data reported by the World Health Organization from January 2017 to December 2019 for influenza and from January 1, 2020 through October 31, 2020, for COVID-19 were used to assess seasonal and temporal trends for influenza and COVID-19 cases across the seven World Bank regions. Results: In regions with more pronounced influenza seasonality, COVID-19 epidemics have largely followed trends similar to those seen for influenza from 2017 to 2019. COVID-19 epidemics in countries across Europe, Central Asia, and North America have been marked by a first peak during the spring, followed by significant reductions in COVID-19 cases in the summer months and a second wave in the fall. In Latin America and the Caribbean, COVID-19 epidemics in several countries peaked in the summer, corresponding to months with the highest influenza activity in the region. Countries from regions with less pronounced influenza activity, including South Asia and sub-Saharan Africa, showed more heterogeneity in COVID-19 epidemics seen to date. However, similarities in COVID-19 and influenza trends were evident within select countries irrespective of region. Conclusions: Ecological consistency in COVID-19 trends seen to date with influenza trends suggests the potential for shared individual, structural, and environmental determinants of transmission. Using a descriptive epidemiological framework to assess shared regional trends for rapidly emerging respiratory pathogens with better studied respiratory infections may provide further insights into the differential impacts of nonpharmacologic interventions and intersections with environmental conditions. Ultimately, forecasting trends and informing interventions for novel respiratory pathogens like COVID-19 should leverage epidemiologic patterns in the relative burden of past respiratory pathogens as prior information. ", doi="10.2196/24696", url="https://publichealth.jmir.org/2021/3/e24696", url="http://www.ncbi.nlm.nih.gov/pubmed/33522974" } @Article{info:doi/10.2196/19712, author="Lwin, Oo May and Lu, Jiahui and Sheldenkar, Anita and Panchapakesan, Chitra and Tan, Yi-Roe and Yap, Peiling and Chen, I. Mark and Chow, TK Vincent and Thoon, Cheng Koh and Yung, Fu Chee and Ang, Wei Li and Ang, SP Brenda", title="Effectiveness of a Mobile-Based Influenza-Like Illness Surveillance System (FluMob) Among Health Care Workers: Longitudinal Study", journal="JMIR Mhealth Uhealth", year="2020", month="Dec", day="7", volume="8", number="12", pages="e19712", keywords="participatory surveillance", keywords="syndromic surveillance", keywords="mobile phone", keywords="influenza-like illness", keywords="health care workers", abstract="Background: Existing studies have suggested that internet-based participatory surveillance systems are a valid sentinel for influenza-like illness (ILI) surveillance. However, there is limited scientific knowledge on the effectiveness of mobile-based ILI surveillance systems. Previous studies also adopted a passive surveillance approach and have not fully investigated the effectiveness of the systems and their determinants. Objective: The aim of this study was to assess the efficiency of a mobile-based surveillance system of ILI, termed FluMob, among health care workers using a targeted surveillance approach. Specifically, this study evaluated the effectiveness of the system for ILI surveillance pertaining to its participation engagement and surveillance power. In addition, we aimed to identify the factors that can moderate the effectiveness of the system. Methods: The FluMob system was launched in two large hospitals in Singapore from April 2016 to March 2018. A total of 690 clinical and nonclinical hospital staff participated in the study for 18 months and were prompted via app notifications to submit a survey listing 18 acute respiratory symptoms (eg, fever, cough, sore throat) on a weekly basis. There was a period of study disruption due to maintenance of the system and the end of the participation incentive between May and July of 2017. Results: On average, the individual submission rate was 41.4\% (SD 24.3\%), with a rate of 51.8\% (SD 26.4\%) before the study disruption and of 21.5\% (SD 30.6\%) after the disruption. Multivariable regression analysis showed that the adjusted individual submission rates were higher for participants who were older (<30 years, 31.4\% vs 31-40 years, 40.2\% [P<.001]; 41-50 years, 46.0\% [P<.001]; >50 years, 39.9\% [P=.01]), ethnic Chinese (Chinese, 44.4\% vs non-Chinese, 34.7\%; P<.001), and vaccinated against flu in the past year (vaccinated, 44.6\% vs nonvaccinated, 34.4\%; P<.001). In addition, the weekly ILI incidence was 1.07\% on average. The Pearson correlation coefficient between ILI incidence estimated by FluMob and that reported by Singapore Ministry of Health was 0.04 (P=.75) with all data and was 0.38 (P=.006) including only data collected before the study disruption. Health care workers with higher risks of ILI and influenza such as women, non-Chinese, allied health staff, those who had children in their households, not vaccinated against influenza, and reported allergy demonstrated higher surveillance correlations. Conclusions: Mobile-based ILI surveillance systems among health care workers can be effective. However, proper operation of the mobile system without major disruptions is vital for the engagement of participants and the persistence of surveillance power. Moreover, the effectiveness of the mobile surveillance system can be moderated by participants' characteristics, which highlights the importance of targeted disease surveillance that can reduce the cost of recruitment and engagement. ", doi="10.2196/19712", url="https://mhealth.jmir.org/2020/12/e19712", url="http://www.ncbi.nlm.nih.gov/pubmed/33284126" } @Article{info:doi/10.2196/21369, author="Choo, Hyunwoo and Kim, Myeongchan and Choi, Jiyun and Shin, Jaewon and Shin, Soo-Yong", title="Influenza Screening via Deep Learning Using a Combination of Epidemiological and Patient-Generated Health Data: Development and Validation Study", journal="J Med Internet Res", year="2020", month="Oct", day="29", volume="22", number="10", pages="e21369", keywords="influenza", keywords="screening tool", keywords="patient-generated health data", keywords="mobile health", keywords="mHealth", keywords="deep learning", abstract="Background: Screening for influenza in primary care is challenging due to the low sensitivity of rapid antigen tests and the lack of proper screening tests. Objective: The aim of this study was to develop a machine learning--based screening tool using patient-generated health data (PGHD) obtained from a mobile health (mHealth) app. Methods: We trained a deep learning model based on a gated recurrent unit to screen influenza using PGHD, including each patient's fever pattern and drug administration records. We used meteorological data and app-based surveillance of the weekly number of patients with influenza. We defined a single episode as the set of consecutive days, including the day the user was diagnosed with influenza or another disease. Any record a user entered 24 hours after his or her last record was considered to be the start of a new episode. Each episode contained data on the user's age, gender, weight, and at least one body temperature record. The total number of episodes was 6657. Of these, there were 3326 episodes within which influenza was diagnosed. We divided these episodes into 80\% training sets (2664/3330) and 20\% test sets (666/3330). A 5-fold cross-validation was used on the training set. Results: We achieved reliable performance with an accuracy of 82\%, a sensitivity of 84\%, and a specificity of 80\% in the test set. After the effect of each input variable was evaluated, app-based surveillance was observed to be the most influential variable. The correlation between the duration of input data and performance was not statistically significant (P=.09). Conclusions: These findings suggest that PGHD from an mHealth app could be a complementary tool for influenza screening. In addition, PGHD, along with traditional clinical data, could be used to improve health conditions. ", doi="10.2196/21369", url="http://www.jmir.org/2020/10/e21369/", url="http://www.ncbi.nlm.nih.gov/pubmed/33118941" } @Article{info:doi/10.2196/16373, author="Wijesundara, G. Jessica and Ito Fukunaga, Mayuko and Ogarek, Jessica and Barton, Bruce and Fisher, Lloyd and Preusse, Peggy and Sundaresan, Devi and Garber, Lawrence and Mazor, M. Kathleen and Cutrona, L. Sarah", title="Electronic Health Record Portal Messages and Interactive Voice Response Calls to Improve Rates of Early Season Influenza Vaccination: Randomized Controlled Trial", journal="J Med Internet Res", year="2020", month="Sep", day="25", volume="22", number="9", pages="e16373", keywords="electronic health records", keywords="influenza vaccination", keywords="patient care", keywords="patient engagement", abstract="Background: Patient reminders for influenza vaccination, delivered via an electronic health record patient portal and interactive voice response calls, offer an innovative approach to engaging patients and improving patient care. Objective: The goal of this study was to test the effectiveness of portal and interactive voice response outreach in improving rates of influenza vaccination by targeting patients in early September, shortly after vaccinations became available. Methods: Using electronic health record portal messages and interactive voice response calls promoting influenza vaccination, outreach was conducted in September 2015. Participants included adult patients within a large multispecialty group practice in central Massachusetts. Our main outcome was electronic health record--documented early influenza vaccination during the 2015-2016 influenza season, measured in November 2015. We randomly assigned all active portal users to 1 of 2 groups: (1) receiving a portal message promoting influenza vaccinations, listing upcoming clinics, and offering online scheduling of vaccination appointments (n=19,506) or (2) receiving usual care (n=19,505). We randomly assigned all portal nonusers to 1 of 2 groups: (1) receiving interactive voice response call (n=15,000) or (2) receiving usual care (n=43,596). The intervention also solicited patient self-reports on influenza vaccinations completed outside the clinic. Self-reported influenza vaccination data were uploaded into the electronic health records to increase the accuracy of existing provider-directed electronic health record clinical decision support (vaccination alerts) but were excluded from main analyses. Results: Among portal users, 28.4\% (5549/19,506) of those randomized to receive messages and 27.1\% (5294/19,505) of the usual care group had influenza vaccinations documented by November 2015 (P=.004). In multivariate analysis of portal users, message recipients were slightly more likely to have documented vaccinations when compared to the usual care group (OR 1.07, 95\% CI 1.02-1.12). Among portal nonusers, 8.4\% (1262/15,000) of those randomized to receive calls and 8.2\% (3586/43,596) of usual care had documented vaccinations (P=.47), and multivariate analysis showed nonsignificant differences. Over half of portal messages sent were opened (10,112/19,479; 51.9\%), and over half of interactive voice response calls placed (7599/14,984; 50.7\%) reached their intended target, thus we attained similar levels of exposure to the messaging for both interventions. Among portal message recipients, 25.4\% of message openers (2570/10,112) responded to a subsequent question on receipt of influenza vaccination; among interactive voice response recipients, 72.5\% of those reached (5513/7599) responded to a similar question. Conclusions: Portal message outreach to a general primary care population achieved a small but statistically significant improvement in rates of influenza vaccination (OR 1.07, 95\% CI 1.02-1.12). Interactive voice response calls did not significantly improve vaccination rates among portal nonusers (OR 1.03, 95\% CI 0.96-1.10). Rates of patient engagement with both modalities were favorable. Trial Registration: ClinicalTrials.gov NCT02266277; https://clinicaltrials.gov/ct2/show/NCT02266277 ", doi="10.2196/16373", url="http://www.jmir.org/2020/9/e16373/", url="http://www.ncbi.nlm.nih.gov/pubmed/32975529" } @Article{info:doi/10.2196/17242, author="Martin, S{\'e}bastien and Maeder, Nirina Muriel and Gon{\c{c}}alves, Rita Ana and Pedrazzini, Baptiste and Perdrix, Jean and Rochat, Carine and Senn, Nicolas and Mueller, Yolanda", title="An Online Influenza Surveillance System for Primary Care Workers in Switzerland: Observational Prospective Pilot Study", journal="JMIR Public Health Surveill", year="2020", month="Sep", day="10", volume="6", number="3", pages="e17242", keywords="influenza", keywords="surveillance system", keywords="primary care", keywords="online", keywords="nosocomial", keywords="transmission", abstract="Background: A better understanding of the influenza epidemiology among primary care workers could guide future recommendations to prevent transmission in primary care practices. Therefore, we designed a pilot study to assess the feasibility of using a work-based online influenza surveillance system among primary care workers. Such an approach is of particular relevance in the context of the coronavirus disease (COVID-19) pandemic, as its findings could apply to other infectious diseases with similar mechanisms of transmission. Objective: This study aims to determine the feasibility of using a work-based online influenza surveillance system for primary care workers in Switzerland. Methods: Physicians and staff of one walk-in clinic and two selected primary care practices were enrolled in this observational prospective pilot study during the 2017-2018 influenza season. They were invited to record symptoms of influenza-like illness in a weekly online survey sent by email and to self-collect a nasopharyngeal swab in case any symptoms were recorded. Samples were tested by real-time polymerase chain reaction for influenza A, influenza B, and a panel of respiratory pathogens. Results: Among 67 eligible staff members, 58\% (n=39) consented to the study and 53\% (n=36) provided data. From the time all participants were included, the weekly survey response rate stayed close to 100\% until the end of the study. Of 79 symptomatic episodes (mean 2.2 episodes per participant), 10 episodes in 7 participants fitted the definition of an influenza-like illness case (attack rate: 7/36, 19\%). One swab tested positive for influenza A H1N1 (attack rate: 3\%, 95\% CI 0\%-18\%). Swabbing was considered relatively easy. Conclusions: A work-based online influenza surveillance system is feasible for use among primary care workers. This promising methodology could be broadly used in future studies to improve the understanding of influenza epidemiology and other diseases such as COVID-19. This could prove to be highly useful in primary care settings and guide future recommendations to prevent transmission. A larger study will also help to assess asymptomatic infections. ", doi="10.2196/17242", url="http://publichealth.jmir.org/2020/3/e17242/", url="http://www.ncbi.nlm.nih.gov/pubmed/32909955" } @Article{info:doi/10.2196/12842, author="Sambaturu, Prathyush and Bhattacharya, Parantapa and Chen, Jiangzhuo and Lewis, Bryan and Marathe, Madhav and Venkatramanan, Srinivasan and Vullikanti, Anil", title="An Automated Approach for Finding Spatio-Temporal Patterns of Seasonal Influenza in the United States: Algorithm Validation Study", journal="JMIR Public Health Surveill", year="2020", month="Sep", day="4", volume="6", number="3", pages="e12842", keywords="epidemic data analysis", keywords="summarization", keywords="spatio-temporal patterns", keywords="transactional data mining", abstract="Background: Agencies such as the Centers for Disease Control and Prevention (CDC) currently release influenza-like illness incidence data, along with descriptive summaries of simple spatio-temporal patterns and trends. However, public health researchers, government agencies, as well as the general public, are often interested in deeper patterns and insights into how the disease is spreading, with additional context. Analysis by domain experts is needed for deriving such insights from incidence data. Objective: Our goal was to develop an automated approach for finding interesting spatio-temporal patterns in the spread of a disease over a large region, such as regions which have specific characteristics (eg, high incidence in a particular week, those which showed a sudden change in incidence) or regions which have significantly different incidence compared to earlier seasons. Methods: We developed techniques from the area of transactional data mining for characterizing and finding interesting spatio-temporal patterns in disease spread in an automated manner. A key part of our approach involved using the principle of minimum description length for representing a given target set in terms of combinations of attributes (referred to as clauses); we considered both positive and negative clauses, relaxed descriptions which approximately represent the set, and used integer programming to find such descriptions. Finally, we designed an automated approach, which examines a large space of sets corresponding to different spatio-temporal patterns, and ranks them based on the ratio of their size to their description length (referred to as the compression ratio). Results: We applied our methods using minimum description length to find spatio-temporal patterns in the spread of seasonal influenza in the United States using state level influenza-like illness activity indicator data from the CDC. We observed that the compression ratios were over 2.5 for 50\% of the chosen sets, when approximate descriptions and negative clauses were allowed. Sets with high compression ratios (eg, over 2.5) corresponded to interesting patterns in the spatio-temporal dynamics of influenza-like illness. Our approach also outperformed description by solution in terms of the compression ratio. Conclusions: Our approach, which is an unsupervised machine learning method, can provide new insights into patterns and trends in the disease spread in an automated manner. Our results show that the description complexity is an effective approach for characterizing sets of interest, which can be easily extended to other diseases and regions beyond influenza in the US. Our approach can also be easily adapted for automated generation of narratives. ", doi="10.2196/12842", url="http://publichealth.jmir.org/2020/3/e12842/", url="http://www.ncbi.nlm.nih.gov/pubmed/32701458" } @Article{info:doi/10.2196/14337, author="Caldwell, K. Wendy and Fairchild, Geoffrey and Del Valle, Y. Sara", title="Surveilling Influenza Incidence With Centers for Disease Control and Prevention Web Traffic Data: Demonstration Using a Novel Dataset", journal="J Med Internet Res", year="2020", month="Jul", day="3", volume="22", number="7", pages="e14337", keywords="influenza", keywords="surveillance", keywords="infoveillance", keywords="infodemiology", keywords="projections and predictions", keywords="internet", keywords="data sources", abstract="Background: Influenza epidemics result in a public health and economic burden worldwide. Traditional surveillance techniques, which rely on doctor visits, provide data with a delay of 1 to 2 weeks. A means of obtaining real-time data and forecasting future outbreaks is desirable to provide more timely responses to influenza epidemics. Objective: This study aimed to present the first implementation of a novel dataset by demonstrating its ability to supplement traditional disease surveillance at multiple spatial resolutions. Methods: We used internet traffic data from the Centers for Disease Control and Prevention (CDC) website to determine the potential usability of this data source. We tested the traffic generated by 10 influenza-related pages in 8 states and 9 census divisions within the United States and compared it against clinical surveillance data. Results: Our results yielded an r2 value of 0.955 in the most successful case, promising results for some cases, and unsuccessful results for other cases. In the interest of scientific transparency to further the understanding of when internet data streams are an appropriate supplemental data source, we also included negative results (ie, unsuccessful models). Models that focused on a single influenza season were more successful than those that attempted to model multiple influenza seasons. Geographic resolution appeared to play a key role, with national and regional models being more successful, overall, than models at the state level. Conclusions: These results demonstrate that internet data may be able to complement traditional influenza surveillance in some cases but not in others. Specifically, our results show that the CDC website traffic may inform national- and division-level models but not models for each individual state. In addition, our results show better agreement when the data were broken up by seasons instead of aggregated over several years. We anticipate that this work will lead to more complex nowcasting and forecasting models using this data stream. ", doi="10.2196/14337", url="https://www.jmir.org/2020/7/e14337", url="http://www.ncbi.nlm.nih.gov/pubmed/32437327" } @Article{info:doi/10.5210/ojphi.v12i1.10576, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2020", volume="12", number="1", pages="e10576", doi="10.5210/ojphi.v12i1.10576", url="", url="http://www.ncbi.nlm.nih.gov/pubmed/32577153" } @Article{info:doi/10.2196/14627, author="Reukers, M. Daphne F. and Marbus, D. Sierk and Smit, Hella and Schneeberger, Peter and Donker, G{\'e} and van der Hoek, Wim and van Gageldonk-Lafeber, B. Arianne", title="Media Reports as a Source for Monitoring Impact of Influenza on Hospital Care: Qualitative Content Analysis", journal="JMIR Public Health Surveill", year="2020", month="Mar", day="4", volume="6", number="1", pages="e14627", keywords="influenza", keywords="severe acute respiratory infections", keywords="SARI", keywords="surveillance", keywords="media reports", keywords="news articles", keywords="hospital care", abstract="Background: The Netherlands, like most European countries, has a robust influenza surveillance system in primary care. However, there is a lack of real-time nationally representative data on hospital admissions for complications of influenza. Anecdotal information about hospital capacity problems during influenza epidemics can, therefore, not be substantiated. Objective: The aim of this study was to assess whether media reports could provide relevant information for estimating the impact of influenza on hospital capacity, in the absence of hospital surveillance data. Methods: Dutch news articles on influenza in hospitals during the influenza season (week 40 of 2017 until week 20 of 2018) were searched in a Web-based media monitoring program (Coosto). Trends in the number of weekly articles were compared with trends in 5 different influenza surveillance systems. A content analysis was performed on a selection of news articles, and information on the hospital, department, problem, and preventive or response measures was collected. Results: The trend in weekly news articles correlated significantly with the trends in all 5 surveillance systems, including severe acute respiratory infections (SARI) surveillance. However, the peak in all 5 surveillance systems preceded the peak in news articles. Content analysis showed hospitals (N=69) had major capacity problems (46/69, 67\%), resulting in admission stops (9/46, 20\%), postponement of nonurgent surgical procedures (29/46, 63\%), or both (8/46, 17\%). Only few hospitals reported the use of point-of-care testing (5/69, 7\%) or a separate influenza ward (3/69, 4\%) to accelerate clinical management, but most resorted to ad hoc crisis management (34/69, 49\%). Conclusions: Media reports showed that the 2017/2018 influenza epidemic caused serious problems in hospitals throughout the country. However, because of the time lag in media reporting, it is not a suitable alternative for near real-time SARI surveillance. A robust SARI surveillance program is important to inform decision making. ", doi="10.2196/14627", url="http://publichealth.jmir.org/2020/1/e14627/", url="http://www.ncbi.nlm.nih.gov/pubmed/32130197" } @Article{info:doi/10.2196/16427, author="Liao, Qiuyan and Fielding, Richard and Cheung, Derek Yee Tak and Lian, Jinxiao and Yuan, Jiehu and Lam, Tak Wendy Wing", title="Effectiveness and Parental Acceptability of Social Networking Interventions for Promoting Seasonal Influenza Vaccination Among Young Children: Randomized Controlled Trial", journal="J Med Internet Res", year="2020", month="Feb", day="28", volume="22", number="2", pages="e16427", keywords="influenza vaccination", keywords="social media", keywords="intervention", keywords="children", abstract="Background: Seasonal influenza vaccination (SIV) coverage among young children remains low worldwide. Mobile social networking apps such as WhatsApp Messenger are promising tools for health interventions. Objective: This was a preliminary study to test the effectiveness and parental acceptability of a social networking intervention that sends weekly vaccination reminders and encourages exchange of SIV-related views and experiences among mothers via WhatsApp discussion groups for promoting childhood SIV. The second objective was to examine the effect of introducing time pressure on mothers' decision making for childhood SIV for vaccination decision making. This was done using countdowns of the recommended vaccination timing. Methods: Mothers of child(ren) aged 6 to 72 months were randomly allocated to control or to one of two social networking intervention groups receiving vaccination reminders with (SNI+TP) or without (SNI--TP) a time pressure component via WhatsApp discussion groups at a ratio of 5:2:2. All participants first completed a baseline assessment. Both the SNI--TP and SNI+TP groups subsequently received weekly vaccination reminders from October to December 2017 and participated in WhatsApp discussions about SIV moderated by a health professional. All participants completed a follow-up assessment from April to May 2018. Results: A total of 84.9\% (174/205), 71\% (57/80), and 75\% (60/80) who were allocated to the control, SNI--TP, and SNI+TP groups, respectively, completed the outcome assessment. The social networking intervention significantly promoted mothers' self-efficacy for taking children for SIV (SNI--TP: odds ratio [OR] 2.69 [1.07-6.79]; SNI+TP: OR 2.50 [1.13-5.55]), but did not result in significantly improved children's SIV uptake. Moreover, after adjusting for mothers' working status, introducing additional time pressure reduced the overall SIV uptake in children of working mothers (OR 0.27 [0.10-0.77]) but significantly increased the SIV uptake among children of mothers without a full-time job (OR 6.53 [1.87-22.82]). Most participants' WhatsApp posts were about sharing experience or views (226/434, 52.1\%) of which 44.7\% (101/226) were categorized as negative, such as their concerns over vaccine safety, side effects and effectiveness. Although participants shared predominantly negative experience or views about SIV at the beginning of the discussion, the moderator was able to encourage the discussion of more positive experience or views and more knowledge and information. Most intervention group participants indicated willingness to receive the same interventions (110/117, 94.0\%) and recommend the interventions to other mothers (102/117, 87.2\%) in future Conclusions: Online information support can effectively promote mothers' self-efficacy for taking children for SIV but alone it may not sufficient to address maternal concerns over SIV to achieve a positive vaccination decision. However, the active involvement of health professionals in online discussions can shape positive discussions about vaccination. Time pressure on decision making interacts with maternal work status, facilitating vaccination uptake among mothers who may have more free time, but having the opposite effect among busier working mothers. Trial Registration: Hong Kong University Clinical Trials Registry HKUCTR-2250; https://tinyurl.com/vejv276 ", doi="10.2196/16427", url="http://www.jmir.org/2020/2/e16427/", url="http://www.ncbi.nlm.nih.gov/pubmed/32130136" } @Article{info:doi/10.2196/12016, author="de Lusignan, Simon and Correa, Ana and Dos Santos, Ga{\"e}l and Meyer, Nadia and Haguinet, Fran{\c{c}}ois and Webb, Rebecca and McGee, Christopher and Byford, Rachel and Yonova, Ivelina and Pathirannehelage, Sameera and Ferreira, Matos Filipa and Jones, Simon", title="Enhanced Safety Surveillance of Influenza Vaccines in General Practice, Winter 2015-16: Feasibility Study", journal="JMIR Public Health Surveill", year="2019", month="Nov", day="14", volume="5", number="4", pages="e12016", keywords="vaccines", keywords="safety management", keywords="medical records systems, computerized", keywords="drug-related side effects and adverse reactions", keywords="influenza, human", keywords="influenza vaccines", keywords="general practice", keywords="England", abstract="Background: The European Medicines Agency (EMA) requires vaccine manufacturers to conduct enhanced real-time surveillance of seasonal influenza vaccination. The EMA has specified a list of adverse events of interest to be monitored. The EMA sets out 3 different ways to conduct such surveillance: (1) active surveillance, (2) enhanced passive surveillance, or (3) electronic health record data mining (EHR-DM). English general practice (GP) is a suitable setting to implement enhanced passive surveillance and EHR-DM. Objective: This study aimed to test the feasibility of conducting enhanced passive surveillance in GP using the yellow card scheme (adverse events of interest reporting cards) to determine if it has any advantages over EHR-DM alone. Methods: A total of 9 GPs in England participated, of which 3 tested the feasibility of enhanced passive surveillance and the other 6 EHR-DM alone. The 3 that tested EPS provided patients with yellow (adverse events) cards for patients to report any adverse events. Data were extracted from all 9 GPs' EHRs between weeks 35 and 49 (08/24/2015 to 12/06/2015), the main period of influenza vaccination. We conducted weekly analysis and end-of-study analyses. Results: Our GPs were largely distributed across England with a registered population of 81,040. In the week 49 report, 15,863/81,040 people (19.57\% of the registered practice population) were vaccinated. In the EPS practices, staff managed to hand out the cards to 61.25\% (4150/6776) of the vaccinees, and of these cards, 1.98\% (82/4150) were returned to the GP offices. Adverse events of interests were reported by 113 /7223 people (1.56\%) in the enhanced passive surveillance practices, compared with 322/8640 people (3.73\%) in the EHR-DM practices. Conclusions: Overall, we demonstrated that GPs EHR-DM was an appropriate method of enhanced surveillance. However, the use of yellow cards, in enhanced passive surveillance practices, did not enhance the collection of adverse events of interests as demonstrated in this study. Their return rate was poor, data entry from them was not straightforward, and there were issues with data reconciliation. We concluded that customized cards prespecifying the EMA's adverse events of interests, combined with EHR-DM, were needed to maximize data collection. International Registered Report Identifier (IRRID): RR2-10.1136/bmjopen-2016-015469 ", doi="10.2196/12016", url="http://publichealth.jmir.org/2019/4/e12016/", url="http://www.ncbi.nlm.nih.gov/pubmed/31724955" } @Article{info:doi/10.2196/14186, author="de Lusignan, Simon and Hoang, Uy and Liyanage, Harshana and Yonova, Ivelina and Ferreira, Filipa and Diez-Domingo, Javier and Clark, Tristan", title="Feasibility of Point-of-Care Testing for Influenza Within a National Primary Care Sentinel Surveillance Network in England: Protocol for a Mixed Methods Study", journal="JMIR Res Protoc", year="2019", month="Nov", day="11", volume="8", number="11", pages="e14186", keywords="diagnosis", keywords="influenza, human", keywords="point-of-care systems", keywords="general practice", abstract="Background: Point-of-care testing (POCT) for influenza promises to provide real-time information to influence clinical decision making and improve patient outcomes. Public Health England has published a toolkit to assist implementation of these tests in the UK National Health Service. Objective: A feasibility study will be undertaken to assess the implementation of influenza POCT in primary care as part of a sentinel surveillance network. Methods: We will conduct a mixed methods study to compare the sampling rates in practices using POCT and current virology swabbing practices not using POCT, and to understand the issues and barriers to implementation of influenza POCT in primary care workflows. The study will take place between March and May 2019. It will be nested in general practices that are part of the English national sentinel surveillance network run by the Royal College of General Practitioners Research and Surveillance Centre. The primary outcome is the number of valid influenza swabs taken and tested by the practices involved in the study using the new POCT. Results: A total of 6 practices were recruited, and data collection commenced on March 11, 2019. Moreover, 312 swab samples had been collected at the time of submission of the protocol, which was 32.5\% (312/960) of the expected sample size. In addition, 68 samples were positive for influenza, which was 20.1\% (68/338) of the expected sample size. Conclusions: To the best of our knowledge, this is the first time an evaluation study has been undertaken on POCT for influenza in general practice in the United Kingdom. This proposed study promises to shed light on the feasibility of implementation of POCT in primary care and on the views of practitioners about the use of influenza POCT in primary care, including its impact on primary care workflows. International Registered Report Identifier (IRRID): DERR1-10.2196/14186 ", doi="10.2196/14186", url="http://www.researchprotocols.org/2019/11/e14186/", url="http://www.ncbi.nlm.nih.gov/pubmed/31710303" } @Article{info:doi/10.2196/14276, author="Kim, Myeongchan and Yune, Sehyo and Chang, Seyun and Jung, Yuseob and Sa, Ok Soon and Han, Wook Hyun", title="The Fever Coach Mobile App for Participatory Influenza Surveillance in Children: Usability Study", journal="JMIR Mhealth Uhealth", year="2019", month="Oct", day="17", volume="7", number="10", pages="e14276", keywords="data collection", keywords="detecting epidemics", keywords="mobile app", keywords="health care app", keywords="influenza epidemics", keywords="influenza in children", abstract="Background: Effective surveillance of influenza requires a broad network of health care providers actively reporting cases of influenza-like illnesses and positive laboratory results. Not only is this traditional surveillance system costly to establish and maintain but there is also a time lag between a change in influenza activity and its detection. A new surveillance system that is both reliable and timely will help public health officials to effectively control an epidemic and mitigate the burden of the disease. Objective: This study aimed to evaluate the use of parent-reported data of febrile illnesses in children submitted through the Fever Coach app in real-time surveillance of influenza activities. Methods: Fever Coach is a mobile app designed to help parents and caregivers manage fever in young children, currently mainly serviced in South Korea. The app analyzes data entered by a caregiver and provides tailored information for care of the child based on the child's age, sex, body weight, body temperature, and accompanying symptoms. Using the data submitted to the app during the 2016-2017 influenza season, we built a regression model that monitors influenza incidence for the 2017-2018 season and validated the model by comparing the predictions with the public influenza surveillance data from the Korea Centers for Disease Control and Prevention (KCDC). Results: During the 2-year study period, 70,203 diagnosis data, including 7702 influenza reports, were submitted. There was a significant correlation between the influenza activity predicted by Fever Coach and that reported by KCDC (Spearman $\rho$=0.878; P<.001). Using this model, the influenza epidemic in the 2017-2018 season was detected 10 days before the epidemic alert announced by KCDC. Conclusions: The Fever Coach app successfully collected data from 7.73\% (207,699/2,686,580) of the target population by providing care instruction for febrile children. These data were used to develop a model that accurately estimated influenza activity measured by the central government agency using reports from sentinel facilities in the national surveillance network. ", doi="10.2196/14276", url="https://mhealth.jmir.org/2019/10/e14276", url="http://www.ncbi.nlm.nih.gov/pubmed/31625946" } @Article{info:doi/10.2196/13403, author="Baltrusaitis, Kristin and Vespignani, Alessandro and Rosenfeld, Roni and Gray, Josh and Raymond, Dorrie and Santillana, Mauricio", title="Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation", journal="JMIR Public Health Surveill", year="2019", month="Sep", day="14", volume="5", number="4", pages="e13403", keywords="digital disease surveillance", keywords="influenza", keywords="surveillance", keywords="participatory syndromic surveillance", keywords="disease modeling", abstract="Background: The Centers for Disease Control and Prevention (CDC) tracks influenza-like illness (ILI) using information on patient visits to health care providers through the Outpatient Influenza-like Illness Surveillance Network (ILINet). As participation in this system is voluntary, the composition, coverage, and consistency of health care reports vary from state to state, leading to different measures of ILI activity between regions. The degree to which these measures reflect actual differences in influenza activity or systematic differences in the methods used to collect and aggregate the data is unclear. Objective: The objective of our study was to qualitatively and quantitatively compare national and region-specific ILI activity in the United States across 4 surveillance data sources---CDC ILINet, Flu Near You (FNY), athenahealth, and HealthTweets.org---to determine whether these data sources, commonly used as input in influenza modeling efforts, show geographical patterns that are similar to those observed in CDC ILINet's data. We also compared the yearly percentage of FNY participants who sought health care for ILI symptoms across geographical areas. Methods: We compared the national and regional 2018-2019 ILI activity baselines, calculated using noninfluenza weeks from previous years, for each surveillance data source. We also compared measures of ILI activity across geographical areas during 3 influenza seasons, 2015-2016, 2016-2017, and 2017-2018. Geographical differences in weekly ILI activity within each data source were also assessed using relative mean differences and time series heatmaps. National and regional age-adjusted health care--seeking percentages were calculated for each influenza season by dividing the number of FNY participants who sought medical care for ILI symptoms by the total number of ILI reports within an influenza season. Pearson correlations were used to assess the association between the health care--seeking percentages and baselines for each surveillance data source. Results: We observed consistent differences in ILI activity across geographical areas for CDC ILINet and athenahealth data. ILI activity for FNY displayed little variation across geographical areas, whereas differences in ILI activity for HealthTweets.org were associated with the total number of tweets within a geographical area. The percentage of FNY participants who sought health care for ILI symptoms differed slightly across geographical areas, and these percentages were positively correlated with CDC ILINet and athenahealth baselines. Conclusions: Our findings suggest that differences in ILI activity across geographical areas as reported by a given surveillance system may not accurately reflect true differences in the prevalence of ILI. Instead, these differences may reflect systematic collection and aggregation biases that are particular to each system and consistent across influenza seasons. These findings are potentially relevant in the real-time analysis of the influenza season and in the definition of unbiased forecast models. ", doi="10.2196/13403", url="https://publichealth.jmir.org/2019/4/e13403", url="http://www.ncbi.nlm.nih.gov/pubmed/31579019" } @Article{info:doi/10.5210/ojphi.v11i2.9952, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2019", volume="11", number="2", pages="e9952", doi="10.5210/ojphi.v11i2.9952", url="", url="http://www.ncbi.nlm.nih.gov/pubmed/31632600" } @Article{info:doi/10.2196/11780, author="Moa, Aye and Muscatello, David and Chughtai, Abrar and Chen, Xin and MacIntyre, Raina C.", title="Flucast: A Real-Time Tool to Predict Severity of an Influenza Season", journal="JMIR Public Health Surveill", year="2019", month="Jul", day="23", volume="5", number="3", pages="e11780", keywords="prediction tool", keywords="influenza", keywords="risk assessment", abstract="Background: Influenza causes serious illness requiring annual health system surge capacity, yet annual seasonal variation makes it difficult to forecast and plan for the severity of an upcoming season. Research shows that hospital and health system stakeholders indicate a preference for forecasting tools that are easy to use and understand to assist with surge capacity planning for influenza. Objective: This study aimed to develop a simple risk prediction tool, Flucast, to predict the severity of an emerging influenza season. Methods: Study data were obtained from the National Notifiable Diseases Surveillance System and Australian Influenza Surveillance Reports from the Department of Health, Australia. We tested Flucast using retrospective seasonal data for 11 Australian influenza seasons. We compared five different models using parameters known early in the season that may be associated with the severity of the season. To calibrate the tool, the resulting estimates of seasonal severity were validated against independent reports of influenza-attributable morbidity and mortality. The model with the highest predictive accuracy against retrospective seasonal activity was chosen as a best-fit model to develop the Flucast tool. The tool was prospectively tested against the 2018 and the emerging 2019 influenza season. Results: The Flucast tool predicted the severity of all retrospectively studied years correctly for influenza seasonal activity in Australia. With the use of real-time data, the tool provided a reasonable early prediction of a low to moderate season for the 2018 and severe seasonal activity for the upcoming 2019 season. The tool meets stakeholder preferences for simplicity and ease of use to assist with surge capacity planning. Conclusions: The Flucast tool may be useful to inform future health system influenza preparedness planning, surge capacity, and intervention programs in real time, and can be adapted for different settings and geographic locations. ", doi="10.2196/11780", url="http://publichealth.jmir.org/2019/3/e11780/", url="http://www.ncbi.nlm.nih.gov/pubmed/31339102" } @Article{info:doi/10.5210/ojphi.v11i1.9670, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2019", volume="11", number="1", pages="e9670", doi="10.5210/ojphi.v11i1.9670", url="" } @Article{info:doi/10.5210/ojphi.v11i1.9724, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2019", volume="11", number="1", pages="e9724", doi="10.5210/ojphi.v11i1.9724", url="" } @Article{info:doi/10.5210/ojphi.v11i1.9739, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2019", volume="11", number="1", pages="e9739", doi="10.5210/ojphi.v11i1.9739", url="" } @Article{info:doi/10.5210/ojphi.v11i1.9748, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2019", volume="11", number="1", pages="e9748", doi="10.5210/ojphi.v11i1.9748", url="" } @Article{info:doi/10.5210/ojphi.v11i1.9752, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2019", volume="11", number="1", pages="e9752", doi="10.5210/ojphi.v11i1.9752", url="" } @Article{info:doi/10.5210/ojphi.v11i1.9757, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2019", volume="11", number="1", pages="e9757", doi="10.5210/ojphi.v11i1.9757", url="" } @Article{info:doi/10.5210/ojphi.v11i1.9758, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2019", volume="11", number="1", pages="e9758", doi="10.5210/ojphi.v11i1.9758", url="" } @Article{info:doi/10.5210/ojphi.v11i1.9785, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2019", volume="11", number="1", pages="e9785", doi="10.5210/ojphi.v11i1.9785", url="" } @Article{info:doi/10.5210/ojphi.v11i1.9834, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2019", volume="11", number="1", pages="e9834", doi="10.5210/ojphi.v11i1.9834", url="" } @Article{info:doi/10.5210/ojphi.v11i1.9844, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2019", volume="11", number="1", pages="e9844", doi="10.5210/ojphi.v11i1.9844", url="" } @Article{info:doi/10.5210/ojphi.v11i1.9873, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2019", volume="11", number="1", pages="e9873", doi="10.5210/ojphi.v11i1.9873", url="" } @Article{info:doi/10.5210/ojphi.v11i1.9877, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2019", volume="11", number="1", pages="e9877", doi="10.5210/ojphi.v11i1.9877", url="" } @Article{info:doi/10.2196/10842, author="Stewart, J. Rebekah and Rossow, John and Eckel, Seth and Bidol, Sally and Ballew, Grant and Signs, Kimberly and Conover, Thelen Julie and Burns, Erin and Bresee, S. Joseph and Fry, M. Alicia and Olsen, J. Sonja and Biggerstaff, Matthew", title="Text-Based Illness Monitoring for Detection of Novel Influenza A Virus Infections During an Influenza A (H3N2)v Virus Outbreak in Michigan, 2016: Surveillance and Survey", journal="JMIR Public Health Surveill", year="2019", month="Apr", day="26", volume="5", number="2", pages="e10842", keywords="influenza", keywords="surveillance", keywords="novel", keywords="agricultural", keywords="fairs", keywords="texting", abstract="Background: Rapid reporting of human infections with novel influenza A viruses accelerates detection of viruses with pandemic potential and implementation of an effective public health response. After detection of human infections with influenza A (H3N2) variant (H3N2v) viruses associated with agricultural fairs during August 2016, the Michigan Department of Health and Human Services worked with the US Centers for Disease Control and Prevention (CDC) to identify infections with variant influenza viruses using a text-based illness monitoring system. Objective: To enhance detection of influenza infections using text-based monitoring and evaluate the feasibility and acceptability of the system for use in future outbreaks of novel influenza viruses. Methods: During an outbreak of H3N2v virus infections among agricultural fair attendees, we deployed a text-illness monitoring (TIM) system to conduct active illness surveillance among households of youth who exhibited swine at fairs. We selected all fairs with suspected H3N2v virus infections. For fairs without suspected infections, we selected only those fairs that met predefined criteria. Eligible respondents were identified and recruited through email outreach and/or on-site meetings at fairs. During the fairs and for 10 days after selected fairs, enrolled households received daily, automated text-messages inquiring about illness; reports of illness were investigated by local health departments. To understand the feasibility and acceptability of the system, we monitored enrollment and trends in participation and distributed a Web-based survey to households of exhibitors from five fairs. Results: Among an estimated 500 households with a member who exhibited swine at one of nine selected fairs, representatives of 87 (17.4\%) households were enrolled, representing 392 household members. Among fairs that were ongoing when the TIM system was deployed, the number of respondents peaked at 54 on the third day of the fair and then steadily declined throughout the rest of the monitoring period; 19 out of 87 household representatives (22\%) responded through the end of the 10-day monitoring period. We detected 2 H3N2v virus infections using the TIM system, which represents 17\% (2/12) of all H3N2v virus infections detected during this outbreak in Michigan. Of the 70 survey respondents, 16 (23\%) had participated in the TIM system. A total of 73\% (11/15) participated because it was recommended by fair coordinators and 80\% (12/15) said they would participate again. Conclusions: Using a text-message system, we monitored for illness among a large number of individuals and households and detected H3N2v virus infections through active surveillance. Text-based illness monitoring systems are useful for detecting novel influenza virus infections when active monitoring is necessary. Participant retention and testing of persons reporting illness are critical elements for system improvement. ", doi="10.2196/10842", url="http://publichealth.jmir.org/2019/2/e10842/", url="http://www.ncbi.nlm.nih.gov/pubmed/31025948" } @Article{info:doi/10.2196/12214, author="Clemente, Leonardo and Lu, Fred and Santillana, Mauricio", title="Improved Real-Time Influenza Surveillance: Using Internet Search Data in Eight Latin American Countries", journal="JMIR Public Health Surveill", year="2019", month="Apr", day="04", volume="5", number="2", pages="e12214", keywords="google flu trends", keywords="influenza monitoring", keywords="real-time disease surveillance", keywords="digital epidemiology", keywords="influenza, human", keywords="developing countries", keywords="machine learning", abstract="Background: Novel influenza surveillance systems that leverage Internet-based real-time data sources including Internet search frequencies, social-network information, and crowd-sourced flu surveillance tools have shown improved accuracy over the past few years in data-rich countries like the United States. These systems not only track flu activity accurately, but they also report flu estimates a week or more ahead of the publication of reports produced by healthcare-based systems, such as those implemented and managed by the Centers for Disease Control and Prevention. Previous work has shown that the predictive capabilities of novel flu surveillance systems, like Google Flu Trends (GFT), in developing countries in Latin America have not yet delivered acceptable flu estimates. Objective: The aim of this study was to show that recent methodological improvements on the use of Internet search engine information to track diseases can lead to improved retrospective flu estimates in multiple countries in Latin America. Methods: A machine learning-based methodology that uses flu-related Internet search activity and historical information to monitor flu activity, named ARGO (AutoRegression with Google search), was extended to generate flu predictions for 8 Latin American countries (Argentina, Bolivia, Brazil, Chile, Mexico, Paraguay, Peru, and Uruguay) for the time period: January 2012 to December of 2016. These retrospective (out-of-sample) Influenza activity predictions were compared with historically observed flu suspected cases in each country, as reported by Flunet, an influenza surveillance database maintained by the World Health Organization. For a baseline comparison, retrospective (out-of-sample) flu estimates were produced for the same time period using autoregressive models that only leverage historical flu activity information. Results: Our results show that ARGO-like models' predictive power outperform autoregressive models in 6 out of 8 countries in the 2012-2016 time period. Moreover, ARGO significantly improves on historical flu estimates produced by the now discontinued GFT for the time period of 2012-2015, where GFT information is publicly available. Conclusions: We demonstrate here that a self-correcting machine learning method, leveraging Internet-based disease-related search activity and historical flu trends, has the potential to produce reliable and timely flu estimates in multiple Latin American countries. This methodology may prove helpful to local public health officials who design and implement interventions aimed at mitigating the effects of influenza outbreaks. Our methodology generally outperforms both the now-discontinued tool GFT, and autoregressive methodologies that exploit only historical flu activity to produce future disease estimates. ", doi="10.2196/12214", url="https://publichealth.jmir.org/2019/2/e12214/", url="http://www.ncbi.nlm.nih.gov/pubmed/30946017" } @Article{info:doi/10.2196/13699, author="Yang, Cheng-Yi and Chen, Ray-Jade and Chou, Wan-Lin and Lee, Yuarn-Jang and Lo, Yu-Sheng", title="Author Contribution Correction: An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation", journal="J Med Internet Res", year="2019", month="Mar", day="12", volume="21", number="3", pages="e13699", doi="10.2196/13699", url="http://www.jmir.org/2019/3/e13699/", url="http://www.ncbi.nlm.nih.gov/pubmed/30860974" } @Article{info:doi/10.2196/12341, author="Yang, Cheng-Yi and Chen, Ray-Jade and Chou, Wan-Lin and Lee, Yuarn-Jang and Lo, Yu-Sheng", title="An Integrated Influenza Surveillance Framework Based on National Influenza-Like Illness Incidence and Multiple Hospital Electronic Medical Records for Early Prediction of Influenza Epidemics: Design and Evaluation", journal="J Med Internet Res", year="2019", month="Feb", day="01", volume="21", number="2", pages="e12341", keywords="influenza", keywords="epidemics", keywords="influenza surveillance", keywords="electronic disease surveillance", keywords="electronic medical records", keywords="electronic health records", keywords="public health", abstract="Background: Influenza is a leading cause of death worldwide and contributes to heavy economic losses to individuals and communities. Therefore, the early prediction of and interventions against influenza epidemics are crucial to reduce mortality and morbidity because of this disease. Similar to other countries, the Taiwan Centers for Disease Control and Prevention (TWCDC) has implemented influenza surveillance and reporting systems, which primarily rely on influenza-like illness (ILI) data reported by health care providers, for the early prediction of influenza epidemics. However, these surveillance and reporting systems show at least a 2-week delay in prediction, indicating the need for improvement. Objective: We aimed to integrate the TWCDC ILI data with electronic medical records (EMRs) of multiple hospitals in Taiwan. Our ultimate goal was to develop a national influenza trend prediction and reporting tool more accurate and efficient than the current influenza surveillance and reporting systems. Methods: First, the influenza expertise team at Taipei Medical University Health Care System (TMUHcS) identified surveillance variables relevant to the prediction of influenza epidemics. Second, we developed a framework for integrating the EMRs of multiple hospitals with the ILI data from the TWCDC website to proactively provide results of influenza epidemic monitoring to hospital infection control practitioners. Third, using the TWCDC ILI data as the gold standard for influenza reporting, we calculated Pearson correlation coefficients to measure the strength of the linear relationship between TMUHcS EMRs and regional and national TWCDC ILI data for 2 weekly time series datasets. Finally, we used the Moving Epidemic Method analyses to evaluate each surveillance variable for its predictive power for influenza epidemics. Results: Using this framework, we collected the EMRs and TWCDC ILI data of the past 3 influenza seasons (October 2014 to September 2017). On the basis of the EMRs of multiple hospitals, 3 surveillance variables, TMUHcS-ILI, TMUHcS-rapid influenza laboratory tests with positive results (RITP), and TMUHcS-influenza medication use (IMU), which reflected patients with ILI, those with positive results from rapid influenza diagnostic tests, and those treated with antiviral drugs, respectively, showed strong correlations with the TWCDC regional and national ILI data (r=.86-.98). The 2 surveillance variables---TMUHcS-RITP and TMUHcS-IMU---showed predictive power for influenza epidemics 3 to 4 weeks before the increase noted in the TWCDC ILI reports. Conclusions: Our framework periodically integrated and compared surveillance data from multiple hospitals and the TWCDC website to maintain a certain prediction quality and proactively provide monitored results. Our results can be extended to other infectious diseases, mitigating the time and effort required for data collection and analysis. Furthermore, this approach may be developed as a cost-effective electronic surveillance tool for the early and accurate prediction of epidemics of influenza and other infectious diseases in densely populated regions and nations. ", doi="10.2196/12341", url="http://www.jmir.org/2019/2/e12341/", url="http://www.ncbi.nlm.nih.gov/pubmed/30707099" } @Article{info:doi/10.2196/11333, author="Naleway, L. Allison and Ball, Sarah and Kwong, C. Jeffrey and Wyant, E. Brandy and Katz, A. Mark and Regan, K. Annette and Russell, L. Margaret and Klein, P. Nicola and Chung, Hannah and Simmonds, A. Kimberley and Azziz-Baumgartner, Eduardo and Feldman, S. Becca and Levy, Avram and Fell, B. Deshayne and Drews, J. Steven and Garg, Shikha and Effler, Paul and Barda, Noam and Irving, A. Stephanie and Shifflett, Patricia and Jackson, L. Michael and Thompson, G. Mark", title="Estimating Vaccine Effectiveness Against Hospitalized Influenza During Pregnancy: Multicountry Protocol for a Retrospective Cohort Study", journal="JMIR Res Protoc", year="2019", month="Jan", day="21", volume="8", number="1", pages="e11333", keywords="influenza", keywords="pregnancy", keywords="hospitalization", keywords="epidemiology", keywords="vaccines", abstract="Background: Although pregnant women are believed to have elevated risks of severe influenza infection and are targeted for influenza vaccination, no study to date has examined influenza vaccine effectiveness (IVE) against laboratory-confirmed influenza-associated hospitalizations during pregnancy, primarily because this outcome poses many methodological challenges. Objective: The Pregnancy Influenza Vaccine Effectiveness Network (PREVENT) was formed in 2016 as an international collaboration with the Centers for Disease Control and Prevention; Abt Associates; and study sites in Australia, Canada, Israel, and the United States. The primary goal of this collaboration is to estimate IVE in preventing acute respiratory or febrile illness (ARFI) hospitalizations associated with laboratory-confirmed influenza virus infection during pregnancy. Secondary aims include (1) describing the incidence, clinical course, and severity of influenza-associated ARFI hospitalization during pregnancy; (2) comparing the characteristics of ARFI-hospitalized pregnant women who were tested for influenza with those who were not tested; (3) describing influenza vaccination coverage in pregnant women; and (4) comparing birth outcomes among women with laboratory-confirmed influenza-associated hospitalization versus other noninfluenza ARFI hospitalizations. Methods: For an initial assessment of IVE, sites identified a retrospective cohort of pregnant women aged from 18 to 50 years whose pregnancies overlapped with local influenza seasons from 2010 to 2016. Pregnancies were defined as those that ended in a live birth or stillbirth of at least 20 weeks gestation. The analytic sample for the primary IVE analysis was restricted to pregnant women who were hospitalized for ARFI during site-specific influenza seasons and clinically tested for influenza virus infection using real-time reverse transcription polymerase chain reaction. Results: We identified approximately 2 million women whose pregnancies overlapped with influenza seasons; 550,344 had at least one hospitalization during this time. After restricting to women who were hospitalized for ARFI and tested for influenza, the IVE analytic sample included 1005 women. Conclusions: In addition to addressing the primary question about the effectiveness of influenza vaccination, PREVENT data will address other important knowledge gaps including understanding the incidence, clinical course, and severity of influenza-related hospitalizations during pregnancy. The data infrastructure and international partnerships created for these analyses may be useful and informative for future influenza studies. International Registered Report Identifier (IRRID): DERR1-10.2196/11333 ", doi="10.2196/11333", url="http://www.researchprotocols.org/2019/1/e11333/", url="http://www.ncbi.nlm.nih.gov/pubmed/30664495" } @Article{info:doi/10.2196/11361, author="Poirier, Canelle and Lavenu, Audrey and Bertaud, Val{\'e}rie and Campillo-Gimenez, Boris and Chazard, Emmanuel and Cuggia, Marc and Bouzill{\'e}, Guillaume", title="Real Time Influenza Monitoring Using Hospital Big Data in Combination with Machine Learning Methods: Comparison Study", journal="JMIR Public Health Surveill", year="2018", month="Dec", day="21", volume="4", number="4", pages="e11361", keywords="electronic health records", keywords="big data", keywords="infodemiology", keywords="infoveillance", keywords="influenza", keywords="machine learning", keywords="Sentinelles network", abstract="Background: Traditional surveillance systems produce estimates of influenza-like illness (ILI) incidence rates, but with 1- to 3-week delay. Accurate real-time monitoring systems for influenza outbreaks could be useful for making public health decisions. Several studies have investigated the possibility of using internet users' activity data and different statistical models to predict influenza epidemics in near real time. However, very few studies have investigated hospital big data. Objective: Here, we compared internet and electronic health records (EHRs) data and different statistical models to identify the best approach (data type and statistical model) for ILI estimates in real time. Methods: We used Google data for internet data and the clinical data warehouse eHOP, which included all EHRs from Rennes University Hospital (France), for hospital data. We compared 3 statistical models---random forest, elastic net, and support vector machine (SVM). Results: For national ILI incidence rate, the best correlation was 0.98 and the mean squared error (MSE) was 866 obtained with hospital data and the SVM model. For the Brittany region, the best correlation was 0.923 and MSE was 2364 obtained with hospital data and the SVM model. Conclusions: We found that EHR data together with historical epidemiological information (French Sentinelles network) allowed for accurately predicting ILI incidence rates for the entire France as well as for the Brittany region and outperformed the internet data whatever was the statistical model used. Moreover, the performance of the two statistical models, elastic net and SVM, was comparable. ", doi="10.2196/11361", url="http://publichealth.jmir.org/2018/4/e11361/", url="http://www.ncbi.nlm.nih.gov/pubmed/30578212" } @Article{info:doi/10.2196/publichealth.8627, author="Wakamiya, Shoko and Kawai, Yukiko and Aramaki, Eiji", title="Twitter-Based Influenza Detection After Flu Peak via Tweets With Indirect Information: Text Mining Study", journal="JMIR Public Health Surveill", year="2018", month="Sep", day="25", volume="4", number="3", pages="e65", keywords="influenza surveillance", keywords="location mention", keywords="Twitter", keywords="social network", keywords="spatial analysis", keywords="internet", keywords="microblog", keywords="infodemiology", keywords="infoveillance", abstract="Background: The recent rise in popularity and scale of social networking services (SNSs) has resulted in an increasing need for SNS-based information extraction systems. A popular application of SNS data is health surveillance for predicting an outbreak of epidemics by detecting diseases from text messages posted on SNS platforms. Such applications share the following logic: they incorporate SNS users as social sensors. These social sensor--based approaches also share a common problem: SNS-based surveillance are much more reliable if sufficient numbers of users are active, and small or inactive populations produce inconsistent results. Objective: This study proposes a novel approach to estimate the trend of patient numbers using indirect information covering both urban areas and rural areas within the posts. Methods: We presented a TRAP model by embedding both direct information and indirect information. A collection of tweets spanning 3 years (7 million influenza-related tweets in Japanese) was used to evaluate the model. Both direct information and indirect information that mention other places were used. As indirect information is less reliable (too noisy or too old) than direct information, the indirect information data were not used directly and were considered as inhibiting direct information. For example, when indirect information appeared often, it was considered as signifying that everyone already had a known disease, leading to a small amount of direct information. Results: The estimation performance of our approach was evaluated using the correlation coefficient between the number of influenza cases as the gold standard values and the estimated values by the proposed models. The results revealed that the baseline model (BASELINE+NLP) shows .36 and that the proposed model (TRAP+NLP) improved the accuracy (.70, +.34 points). Conclusions: The proposed approach by which the indirect information inhibits direct information exhibited improved estimation performance not only in rural cities but also in urban cities, which demonstrated the effectiveness of the proposed method consisting of a TRAP model and natural language processing (NLP) classification. ", doi="10.2196/publichealth.8627", url="http://publichealth.jmir.org/2018/3/e65/", url="http://www.ncbi.nlm.nih.gov/pubmed/30274968" } @Article{info:doi/10.2196/mhealth.9834, author="Fujibayashi, Kazutoshi and Takahashi, Hiromizu and Tanei, Mika and Uehara, Yuki and Yokokawa, Hirohide and Naito, Toshio", title="A New Influenza-Tracking Smartphone App (Flu-Report) Based on a Self-Administered Questionnaire: Cross-Sectional Study", journal="JMIR Mhealth Uhealth", year="2018", month="Jun", day="06", volume="6", number="6", pages="e136", keywords="influenza", keywords="epidemiology", keywords="pandemics", keywords="internet", keywords="participatory surveillance", keywords="participatory epidemiology", abstract="Background: Influenza infections can spread rapidly, and influenza outbreaks are a major public health concern worldwide. Early detection of signs of an influenza pandemic is important to prevent global outbreaks. Development of information and communications technologies for influenza surveillance, including participatory surveillance systems involving lay users, has recently increased. Many of these systems can estimate influenza activity faster than the conventional influenza surveillance systems. Unfortunately, few of these influenza-tracking systems are available in Japan. Objective: This study aimed to evaluate the flu-tracking ability of Flu-Report, a new influenza-tracking mobile phone app that uses a self-administered questionnaire for the early detection of influenza activity. Methods: Flu-Report was used to collect influenza-related information (ie, dates on which influenza infections were diagnosed) from November 2016 to March 2017. Participants were adult volunteers from throughout Japan, who also provided information about their cohabiting family members. The utility of Flu-Report was evaluated by comparison with the conventional influenza surveillance information and basic information from an existing large-scale influenza-tracking system (an automatic surveillance system based on electronic records of prescription drug purchases). Results: Information was obtained through Flu-Report for approximately 10,094 volunteers. In total, 2134 participants were aged <20 years, 6958 were aged 20-59 years, and 1002 were aged ?60 years. Between November 2016 and March 2017, 347 participants reported they had influenza or an influenza-like illness in the 2016 season. Flu-Report-derived influenza infection time series data displayed a good correlation with basic information obtained from the existing influenza surveillance system (rho, $\rho$=.65, P=.001). However, the influenza morbidity ratio for our participants was approximately 25\% of the mean influenza morbidity ratio for the Japanese population. The Flu-Report influenza morbidity ratio was 5.06\% (108/2134) among those aged <20 years, 3.16\% (220/6958) among those aged 20-59 years, and 0.59\% (6/1002) among those aged ?60 years. In contrast, influenza morbidity ratios for Japanese individuals aged <20 years, 20-59 years, and ?60 years were recently estimated at 31.97\% to 37.90\%, 8.16\% to 9.07\%, and 2.71\% to 4.39\%, respectively. Conclusions: Flu-Report supports easy access to near real-time information about influenza activity via the accumulation of self-administered questionnaires. However, Flu-Report users may be influenced by selection bias, which is a common issue associated with surveillance using information and communications technologies. Despite this, Flu-Report has the potential to provide basic data that could help detect influenza outbreaks. ", doi="10.2196/mhealth.9834", url="http://mhealth.jmir.org/2018/6/e136/" } @Article{info:doi/10.5210/ojphi.v10i1.8757, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2018", volume="10", number="1", pages="e8757", doi="10.5210/ojphi.v10i1.8757", url="" } @Article{info:doi/10.5210/ojphi.v10i1.8773, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2018", volume="10", number="1", pages="e8773", doi="10.5210/ojphi.v10i1.8773", url="" } @Article{info:doi/10.5210/ojphi.v10i1.8921, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2018", volume="10", number="1", pages="e8921", doi="10.5210/ojphi.v10i1.8921", url="" } @Article{info:doi/10.5210/ojphi.v10i1.8925, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2018", volume="10", number="1", pages="e8925", doi="10.5210/ojphi.v10i1.8925", url="" } @Article{info:doi/10.5210/ojphi.v10i1.8939, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2018", volume="10", number="1", pages="e8939", doi="10.5210/ojphi.v10i1.8939", url="" } @Article{info:doi/10.5210/ojphi.v10i1.8948, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2018", volume="10", number="1", pages="e8948", doi="10.5210/ojphi.v10i1.8948", url="" } @Article{info:doi/10.5210/ojphi.v10i1.8990, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2018", volume="10", number="1", pages="e8990", doi="10.5210/ojphi.v10i1.8990", url="" } @Article{info:doi/10.2196/publichealth.8874, author="Chan, Ta-Chien and Hu, Tsuey-Hwa and Hwang, Jing-Shiang", title="Estimating the Risk of Influenza-Like Illness Transmission Through Social Contacts: Web-Based Participatory Cohort Study", journal="JMIR Public Health Surveill", year="2018", month="Apr", day="09", volume="4", number="2", pages="e40", keywords="flu transmission", keywords="social networks", keywords="contact diary", keywords="diet", keywords="exercise", keywords="sleep quality", abstract="Background: Epidemiological studies on influenza have focused mostly on enhancing vaccination coverage or promoting personal hygiene behavior. Few studies have investigated potential effects of personal health behaviors and social contacts on the risk of getting influenza-like illness (ILI). Objective: Taking advantage of an online participatory cohort, this study aimed to estimate the increased risk of getting ILI after contact with infected persons and examine how personal health behaviors, weather, and air pollution affect the probability of getting ILI. Methods: A Web-based platform was designed for participants to record daily health behaviors and social contacts during the influenza season of October 1, 2015 to March 31, 2016, in Taiwan. Data on sleep, diet, physical activity, self-reported ILI, and contact with infected persons were retrieved from the diaries. Measurements of weather and air pollutants were used for calculating environmental exposure levels for the participants. We fitted a mixed-effects logistic regression model to the daily measurements of the diary keepers to estimate the effects of these variables on the risk of getting ILI. Results: During the influenza season, 160 participants provided 14,317 health diaries and recorded 124,222 face-to-face contacts. The model estimated odds ratio of getting ILI was 1.87 (95\% CI 1.40-2.50) when a person had contact with others having ILI in the previous 3 days. Longer duration of physical exercise and eating more fruits, beans, and dairy products were associated with lower risk of getting ILI. However, staying up late was linked to an elevated risk of getting ILI. Higher variation of ambient temperature and worse air quality were associated with increased risk of developing ILI. Conclusions: Developing a healthier lifestyle, avoiding contact with persons having ILI symptoms, and staying alert with respect to temperature changes and air quality can reduce the risk of getting ILI. ", doi="10.2196/publichealth.8874", url="http://publichealth.jmir.org/2018/2/e40/", url="http://www.ncbi.nlm.nih.gov/pubmed/29631987" } @Article{info:doi/10.2196/10640, author="Farooq Tahir, Muhammad", title="Surveillance and Molecular Epidemiology of Avian Influenza H9N2 Viruses Circulating in Pakistan", journal="iproc", year="2018", month="Mar", day="29", volume="4", number="1", pages="e10640", abstract="Background: Avian influenza H9N2 is highly endemic in commercial and backyard poultry in Pakistan. Its widespread circulation and high mutation rates provide a possibility of novel reassorted viruses hence posing a serious public health threat. Objective: This study was aimed to isolate and evaluate the AI H9N2 viruses circulating in poultry as well as aquatic birds in Pakistan between 2014 and 2017. Methods: Specimens were collected from morbid or dead birds suspected for AI H9N2 on the basis of clinical signs or post-mortem lesions brought to five poultry diagnostic laboratories in Punjab. The samples were subjected for virus isolation. The isolates then were confirmed for H and N type using PCR. Six isolates were subjected to phylogenetic analysis of Haemagglutinin gene. The results were compared with isolate reported previously from Pakistan and other regional countries for homology. Results: 129,622 samples from 7481 poultry flocks were processed, 5.3\% (399/7481) were positive for AIV H9N2. Sequence analysis showed that it had homology of 84-93\% with different regional strains. Changes were seen at 24 different sites and at cleavage site at K148R and I151R in comparison to previous Pakistani isolates. Six possible glycosylation sites were observed. Neighbor joining phylogenetic tree confirmed its 93.4\% homology with the isolate of Iran. The isolates were the same clade as other regional isolates and have common ancestors. Conclusions: The prevailing H9N2 viruses in Pakistan have certain markers and elements in the HA gene that may improve its avian to human transmission. Continuous surveillance of influenza A viruses is necessary to monitor their antigenic determinants. Protocols for the AI surveillance have officially been notified by Department of Livestock \& Dairy Development Department, Punjab as a result of these findings. ", doi="10.2196/10640", url="http://www.iproc.org/2018/1/e10640/" } @Article{info:doi/10.2196/10619, author="Noreen, Nadia", title="Evaluation of Lab-based Influenza Surveillance System in Pakistan, 2017", journal="iproc", year="2018", month="Mar", day="29", volume="4", number="1", pages="e10619", abstract="Background: Globally 5-10\% of adults and 20-30\% of the children are affected by influenza annually. Annual epidemics results in 3-5 million cases and 500,000 deaths. Influenza is a common illness in Pakistan however absence of a robust surveillance system makes assessment of burden of disease an issue. Objective: The study was conducted to identify key strengths and weaknesses of the system and to make recommendations based on findings. Methods: An evaluative descriptive study was conducted from April to July 2017. The Lab-based Influenza Surveillance System was conducted at the national level. Assessment of qualitative and quantitative system attributes was done utilizing the CDC's Updated Guidelines for Evaluating Public Health Surveillance Systems, 2001. Desk review of literature, departmental documents and reports were also conducted. The stakeholders were identified and interviewed using a semi-structured questionnaire. Results: The system was found to be simple and easy to operate but less flexible to integrate with other diseases. Data quality was good as 80\% of observed forms were completely filled. Timeliness was good as the data takes 24-48 hours from sample collection to report submission to the central level. Acceptability is good as private and public-sector hospitals and labs are involved. Sensitivity calculated was 62\% and Predictive Value Positive (PVP) was 37.2\%. The representativeness of Lab based influenza surveillance system is poor as it is a sentinel surveillance with specific reporting sites strategically placed. Data from all sentinel sites is analyzed at national reference lab where it is summarized to use for planning and management purposes. Conclusions: The system is meeting its objectives. Sustainability and stability of the system needs to be improved by allocation of public funds. Extension of the coverage of the system will result in improved representativeness. Regular capacity building of the staff at reporting site will ensure continued quality of reporting. ", doi="10.2196/10619", url="http://www.iproc.org/2018/1/e10619/" } @Article{info:doi/10.2196/10599, author="Al Amad, Mohammed", title="Severe Acute Respiratory Infections with Influenza and Non-Influenza Respiratory Viruses: Yemen, 2011-2016", journal="iproc", year="2018", month="Mar", day="29", volume="4", number="1", pages="e10599", abstract="Background: Sentinel surveillance for severe acute respiratory infections (SARI) is an important tool to monitor influenza circulation and burden of other respiratory pathogens. In Yemen, two sites established at Sana'a and Aden city. Pharyngeal samples are tested for influenza and non- influenza by the Real-Time-PCR assay in NAMRU 3. Objective: Describe severity of SARI as indicated by admission to intensive care unit (ICU) and fatality as well as associated influenza and non-influenza viruses among patients in the two sites to provide recommendations for improving SARI surveillance. Methods: Data from 2012-2016 of SARI patients who admitted in the two sites based on WHO case definition was obtained from Ministry of Health, It analyzed by Epi info 7 and P<0.05 was the cut point for significance. Results: 2,211 patients were admitted in the two sites, 32\% in 2013, 62\% from Aden, 63\% < two years, 20\% had chronic diseases and 35\% admitted to ICU. Overall SARI fatality was 8\% which was significantly higher in Aden than Sana'a (10\% vs. 5\%, P<0.001), among patients with chronic disease (14\% vs. 6.5\% P<0.001) and admitted to ICU (10\% vs. 7\%, P=0.04). Samples of 82\% (1,811) patients were tested where influenza viruses (75\% Type A) were detected in 5\% (89) more in Sana'a than Aden (6\% vs. 4\%, P=0.04) compared to 36\% (655) of non-influenza viruses that included 43\% (279) Respiratory Syncytial Virus and 17\% (109) Adenovirus. The fatality of confirmed influenza was 9 \% compared to 8\% for non-influenza viruses. Conclusions: Our findings showed that children < 2 years are more affected by SARI. Both influenza and non-influenza viruses lead to mortality and necessitate prompt diagnosis and treatment. Expanding SARI surveillance to involve more hospitals at different governorate is recommended to give more comprehensive picture regarding SARI. ", doi="10.2196/10599", url="http://www.iproc.org/2018/1/e10599/" } @Article{info:doi/10.2196/10600, author="AbdElGawad, Basma and Refaey, S. and Abu El Sood, H. and El Shourbagy, S. and Mohsen, A. and Fahim, M.", title="Defining Influenza Baseline and Threshold Values Using Surveillance Data - Egypt, Season 2016-17", journal="iproc", year="2018", month="Mar", day="29", volume="4", number="1", pages="e10600", abstract="Background: Influenza infection represents a substantial public health problem resulting in global burden of mortality and morbidity. Influenza thresholds indicate level of disease activity that would signal the start or end of a season and provide an alert to an unusually severe or atypical season so, adjust preventive and control measures. Objective: To establish baseline and threshold values for 2016/17 season. Methods: Using Acute Respiratory Illness (ARI) surveillance data from 2013 to 2017, two parameters were assessed to monitor influenza activity: percentage of ARI samples positive for influenza and composite parameter (percentage of samples tested positive *ARI rate). Three threshold levels (baseline, alert and epidemic) were established by calculation of average of each week in all preceding seasons, 40\% Upper Confidence Limit (UCL) and 90\% UCL of each week respectively, then a four-week running average used to smooth the curve. Each parameter was compared against corresponding threshold and transmission intensity was categorized as low, moderate and high. Results: For season 2016/2017, both parameters showed two waves of activity crossing baseline threshold. First started at week 35 to 45 with dominance of Flu A/H3 activity (293(89) \% of 329 positive samples, remain was Flu B) that exceeds epidemic threshold. The other, started week 12 to 14 with dominance of Flu B activity (136(99) \% of 138 positive samples, remain was Flu A/H3). Percentage positive parameter signaled other weeks away from the defined season. Conclusions: Public health actions were taken in response to the observed increase flu A/H3 activity, to trim the impact and serious consequences of the disease. Continuous calculation of baseline and threshold levels can assess not only seasonal influenza but also potential pandemic influenza, contributing to the country's pandemic preparedness and have important implications especially for resource-limited countries. ", doi="10.2196/10600", url="http://www.iproc.org/2018/1/e10600/" } @Article{info:doi/10.2196/publichealth.8198, author="Chen, Bin and Shao, Jian and Liu, Kui and Cai, Gaofeng and Jiang, Zhenggang and Huang, Yuru and Gu, Hua and Jiang, Jianmin", title="Does Eating Chicken Feet With Pickled Peppers Cause Avian Influenza? Observational Case Study on Chinese Social Media During the Avian Influenza A (H7N9) Outbreak", journal="JMIR Public Health Surveill", year="2018", month="Mar", day="29", volume="4", number="1", pages="e32", keywords="social media", keywords="misinformation", keywords="infodemiology", keywords="avian influenza A", keywords="disease outbreak", abstract="Background: A hot topic on the relationship between a popular avian-origin food and avian influenza occurred on social media during the outbreak of the emerging avian influenza A (H7N9). The misinformation generated from this topic had caused great confusion and public concern. Objective: Our goals were to analyze the trend and contents of the relevant posts during the outbreak. We also aimed to understand the characteristics of the misinformation and to provide suggestions to reduce public misconception on social media during the emerging disease outbreak. Methods: The original microblog posts were collected from China's Sina Weibo and Tencent Weibo using a combination of keywords between April 1, 2013 and June 2, 2013. We analyzed the weekly and daily trend of the relevant posts. Content analyses were applied to categorize the posts into 4 types with unified sorting criteria. The posts' characteristics and geographic locations were also analyzed in each category. We conducted further analysis on the top 5 most popular misleading posts. Results: A total of 1680 original microblog posts on the topic were retrieved and 341 (20.30\%) of these posts were categorized as misleading messages. The number of relevant posts had not increased much during the first 2 weeks but rose to a high level in the next 2 weeks after the sudden increase in number of reported cases at the beginning of week 3. The posts under ``misleading messages'' occurred and increased from the beginning of week 3, but their daily posting number decreased when the daily number of posts under ``refuting messages'' outnumbered them. The microbloggers of the misleading posts had the lowest mean rank of followers and previous posts, but their posts had a highest mean rank of posts. The proportion of ``misleading messages'' in places with no reported cases was significantly higher than that in the epidemic areas (23.6\% vs 13.8\%). The popular misleading posts appeared to be short and consisted of personal narratives, which were easily disseminated on social media. Conclusions: Our findings suggested the importance of responding to common questions and misconceptions on social media platforms from the beginning of disease outbreaks. Authorities need to release clear and reliable information related to the popular topics early on. The microbloggers posting correct information should be empowered and their posts could be promoted to clarify false information. Equal importance should be attached to clarify misinformation in both the outbreak and nonoutbreak areas. ", doi="10.2196/publichealth.8198", url="http://publichealth.jmir.org/2018/1/e32/", url="http://www.ncbi.nlm.nih.gov/pubmed/29599109" } @Article{info:doi/10.2196/jmir.9084, author="Wenham, Clare and Gray, R. Eleanor and Keane, E. Candice and Donati, Matthew and Paolotti, Daniela and Pebody, Richard and Fragaszy, Ellen and McKendry, A. Rachel and Edmunds, John W.", title="Self-Swabbing for Virological Confirmation of Influenza-Like Illness Among an Internet-Based Cohort in the UK During the 2014-2015 Flu Season: Pilot Study", journal="J Med Internet Res", year="2018", month="Mar", day="01", volume="20", number="3", pages="e71", keywords="influenza", keywords="influenza-like illness", keywords="surveillance", keywords="online", keywords="cohort study", keywords="virological confirmation", abstract="Background: Routine influenza surveillance, based on laboratory confirmation of viral infection, often fails to estimate the true burden of influenza-like illness (ILI) in the community because those with ILI often manage their own symptoms without visiting a health professional. Internet-based surveillance can complement this traditional surveillance by measuring symptoms and health behavior of a population with minimal time delay. Flusurvey, the UK's largest crowd-sourced platform for surveillance of influenza, collects routine data on more than 6000 voluntary participants and offers real-time estimates of ILI circulation. However, one criticism of this method of surveillance is that it is only able to assess ILI, rather than virologically confirmed influenza. Objective: We designed a pilot study to see if it was feasible to ask individuals from the Flusurvey platform to perform a self-swabbing task and to assess whether they were able to collect samples with a suitable viral content to detect an influenza virus in the laboratory. Methods: Virological swabbing kits were sent to pilot study participants, who then monitored their ILI symptoms over the influenza season (2014-2015) through the Flusurvey platform. If they reported ILI, they were asked to undertake self-swabbing and return the swabs to a Public Health England laboratory for multiplex respiratory virus polymerase chain reaction testing. Results: A total of 700 swab kits were distributed at the start of the study; from these, 66 participants met the definition for ILI and were asked to return samples. In all, 51 samples were received in the laboratory, 18 of which tested positive for a viral cause of ILI (35\%). Conclusions: This demonstrated proof of concept that it is possible to apply self-swabbing for virological laboratory testing to an online cohort study. This pilot does not have significant numbers to validate whether Flusurvey surveillance accurately reflects influenza infection in the community, but highlights that the methodology is feasible. Self-swabbing could be expanded to larger online surveillance activities, such as during the initial stages of a pandemic, to understand community transmission or to better assess interseasonal activity. ", doi="10.2196/jmir.9084", url="http://www.jmir.org/2018/3/e71/", url="http://www.ncbi.nlm.nih.gov/pubmed/29496658" } @Article{info:doi/10.2196/publichealth.8950, author="Lu, Sun Fred and Hou, Suqin and Baltrusaitis, Kristin and Shah, Manan and Leskovec, Jure and Sosic, Rok and Hawkins, Jared and Brownstein, John and Conidi, Giuseppe and Gunn, Julia and Gray, Josh and Zink, Anna and Santillana, Mauricio", title="Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis", journal="JMIR Public Health Surveill", year="2018", month="Jan", day="09", volume="4", number="1", pages="e4", keywords="epidemiology", keywords="public health", keywords="machine learning", keywords="regression analysis", keywords="influenza, human", keywords="communicable diseases", keywords="statistics", keywords="patient generated data", abstract="Background: Influenza outbreaks pose major challenges to public health around the world, leading to thousands of deaths a year in the United States alone. Accurate systems that track influenza activity at the city level are necessary to provide actionable information that can be used for clinical, hospital, and community outbreak preparation. Objective: Although Internet-based real-time data sources such as Google searches and tweets have been successfully used to produce influenza activity estimates ahead of traditional health care--based systems at national and state levels, influenza tracking and forecasting at finer spatial resolutions, such as the city level, remain an open question. Our study aimed to present a precise, near real-time methodology capable of producing influenza estimates ahead of those collected and published by the Boston Public Health Commission (BPHC) for the Boston metropolitan area. This approach has great potential to be extended to other cities with access to similar data sources. Methods: We first tested the ability of Google searches, Twitter posts, electronic health records, and a crowd-sourced influenza reporting system to detect influenza activity in the Boston metropolis separately. We then adapted a multivariate dynamic regression method named ARGO (autoregression with general online information), designed for tracking influenza at the national level, and showed that it effectively uses the above data sources to monitor and forecast influenza at the city level 1 week ahead of the current date. Finally, we presented an ensemble-based approach capable of combining information from models based on multiple data sources to more robustly nowcast as well as forecast influenza activity in the Boston metropolitan area. The performances of our models were evaluated in an out-of-sample fashion over 4 influenza seasons within 2012-2016, as well as a holdout validation period from 2016 to 2017. Results: Our ensemble-based methods incorporating information from diverse models based on multiple data sources, including ARGO, produced the most robust and accurate results. The observed Pearson correlations between our out-of-sample flu activity estimates and those historically reported by the BPHC were 0.98 in nowcasting influenza and 0.94 in forecasting influenza 1 week ahead of the current date. Conclusions: We show that information from Internet-based data sources, when combined using an informed, robust methodology, can be effectively used as early indicators of influenza activity at fine geographic resolutions. ", doi="10.2196/publichealth.8950", url="http://publichealth.jmir.org/2018/1/e4/", url="http://www.ncbi.nlm.nih.gov/pubmed/29317382" } @Article{info:doi/10.2196/jmir.8184, author="Wagner, Moritz and Lampos, Vasileios and Yom-Tov, Elad and Pebody, Richard and Cox, J. Ingemar", title="Estimating the Population Impact of a New Pediatric Influenza Vaccination Program in England Using Social Media Content", journal="J Med Internet Res", year="2017", month="Dec", day="21", volume="19", number="12", pages="e416", keywords="health intervention", keywords="influenza", keywords="vaccination", keywords="social media", keywords="Twitter", abstract="Background: The rollout of a new childhood live attenuated influenza vaccine program was launched in England in 2013, which consisted of a national campaign for all 2 and 3 year olds and several pilot locations offering the vaccine to primary school-age children (4-11 years of age) during the influenza season. The 2014/2015 influenza season saw the national program extended to include additional pilot regions, some of which offered the vaccine to secondary school children (11-13 years of age) as well. Objective: We utilized social media content to obtain a complementary assessment of the population impact of the programs that were launched in England during the 2013/2014 and 2014/2015 flu seasons. The overall community-wide impact on transmission in pilot areas was estimated for the different age groups that were targeted for vaccination. Methods: A previously developed statistical framework was applied, which consisted of a nonlinear regression model that was trained to infer influenza-like illness (ILI) rates from Twitter posts originating in pilot (school-age vaccinated) and control (unvaccinated) areas. The control areas were then used to estimate ILI rates in pilot areas, had the intervention not taken place. These predictions were compared with their corresponding Twitter-based ILI estimates. Results: Results suggest a reduction in ILI rates of 14\% (1-25\%) and 17\% (2-30\%) across all ages in only the primary school-age vaccine pilot areas during the 2013/2014 and 2014/2015 influenza seasons, respectively. No significant impact was observed in areas where two age cohorts of secondary school children were vaccinated. Conclusions: These findings corroborate independent assessments from traditional surveillance data, thereby supporting the ongoing rollout of the program to primary school-age children and providing evidence of the value of social media content as an additional syndromic surveillance tool. ", doi="10.2196/jmir.8184", url="http://www.jmir.org/2017/12/e416/", url="http://www.ncbi.nlm.nih.gov/pubmed/29269339" } @Article{info:doi/10.2196/publichealth.8015, author="Samaras, Loukas and Garc{\'i}a-Barriocanal, Elena and Sicilia, Miguel-Angel", title="Syndromic Surveillance Models Using Web Data: The Case of Influenza in Greece and Italy Using Google Trends", journal="JMIR Public Health Surveill", year="2017", month="Nov", day="20", volume="3", number="4", pages="e90", keywords="Google Trends", keywords="influenza", keywords="Web, syndromic surveillance", keywords="statistical correlation", keywords="forecast", keywords="ARIMA", abstract="Background: An extended discussion and research has been performed in recent years using data collected through search queries submitted via the Internet. It has been shown that the overall activity on the Internet is related to the number of cases of an infectious disease outbreak. Objective: The aim of the study was to define a similar correlation between data from Google Trends and data collected by the official authorities of Greece and Europe by examining the development and the spread of seasonal influenza in Greece and Italy. Methods: We used multiple regressions of the terms submitted in the Google search engine related to influenza for the period from 2011 to 2012 in Greece and Italy (sample data for 104 weeks for each country). We then used the autoregressive integrated moving average statistical model to determine the correlation between the Google search data and the real influenza cases confirmed by the aforementioned authorities. Two methods were used: (1) a flu score was created for the case of Greece and (2) comparison of data from a neighboring country of Greece, which is Italy. Results: The results showed that there is a significant correlation that can help the prediction of the spread and the peak of the seasonal influenza using data from Google searches. The correlation for Greece for 2011 and 2012 was .909 and .831, respectively, and correlation for Italy for 2011 and 2012 was .979 and .933, respectively. The prediction of the peak was quite precise, providing a forecast before it arrives to population. Conclusions: We can create an Internet surveillance system based on Google searches to track influenza in Greece and Italy. ", doi="10.2196/publichealth.8015", url="http://publichealth.jmir.org/2017/4/e90/", url="http://www.ncbi.nlm.nih.gov/pubmed/29158208" } @Article{info:doi/10.2196/publichealth.8610, author="Prieto, Tom{\'a}s Jos{\'e} and Jara, H. Jorge and Alvis, Pablo Juan and Furlan, R. Luis and Murray, Travis Christian and Garcia, Judith and Benghozi, Pierre-Jean and Kaydos-Daniels, Cornelia Susan", title="Will Participatory Syndromic Surveillance Work in Latin America? Piloting a Mobile Approach to Crowdsource Influenza-Like Illness Data in Guatemala", journal="JMIR Public Health Surveill", year="2017", month="Nov", day="14", volume="3", number="4", pages="e87", keywords="crowdsourcing", keywords="human flu", keywords="influenza", keywords="grippe", keywords="mHealth", keywords="texting", keywords="mobile apps", keywords="short message service", keywords="text message", keywords="developing countries", abstract="Background: In many Latin American countries, official influenza reports are neither timely nor complete, and surveillance of influenza-like illness (ILI) remains thin in consistency and precision. Public participation with mobile technology may offer new ways of identifying nonmedically attended cases and reduce reporting delays, but no published studies to date have assessed the viability of ILI surveillance with mobile tools in Latin America. We implemented and assessed an ILI-tailored mobile health (mHealth) participatory reporting system. Objective: The objectives of this study were to evaluate the quality and characteristics of electronically collected data, the user acceptability of the symptom reporting platform, and the costs of running the system and of identifying ILI cases, and to use the collected data to characterize cases of reported ILI. Methods: We recruited the heads of 189 households comprising 584 persons during randomly selected home visits in Guatemala. From August 2016 to March 2017, participants used text messages or an app to report symptoms of ILI at home, the ages of the ILI cases, if medical attention was sought, and if medicines were bought in pharmacies. We sent weekly reminders to participants and compensated those who sent reports with phone credit. We assessed the simplicity, flexibility, acceptability, stability, timeliness, and data quality of the system. Results: Nearly half of the participants (47.1\%, 89/189) sent one or more reports. We received 468 reports, 83.5\% (391/468) via text message and 16.4\% (77/468) via app. Nine-tenths of the reports (93.6\%, 438/468) were received within 48 hours of the transmission of reminders. Over a quarter of the reports (26.5\%, 124/468) indicated that at least someone at home had ILI symptoms. We identified 202 ILI cases and collected age information from almost three-fifths (58.4\%, 118/202): 20 were aged between 0 and 5 years, 95 were aged between 6 and 64 years, and three were aged 65 years or older. Medications were purchased from pharmacies, without medical consultation, in 33.1\% (41/124) of reported cases. Medical attention was sought in 27.4\% (34/124) of reported cases. The cost of identifying an ILI case was US \$6.00. We found a positive correlation (Pearson correlation coefficient=.8) between reported ILI and official surveillance data for noninfluenza viruses from weeks 41 (2016) to 13 (2017). Conclusions: Our system has the potential to serve as a practical complement to respiratory virus surveillance in Guatemala. Its strongest attributes are simplicity, flexibility, and timeliness. The biggest challenge was low enrollment caused by people's fear of victimization and lack of phone credit. Authorities in Central America could test similar methods to improve the timeliness, and extend the breadth, of disease surveillance. It may allow them to rapidly detect localized or unusual circulation of acute respiratory illness and trigger appropriate public health actions. ", doi="10.2196/publichealth.8610", url="http://publichealth.jmir.org/2017/4/e87/", url="http://www.ncbi.nlm.nih.gov/pubmed/29138128" } @Article{info:doi/10.2196/jmir.7486, author="Kandula, Sasikiran and Hsu, Daniel and Shaman, Jeffrey", title="Subregional Nowcasts of Seasonal Influenza Using Search Trends", journal="J Med Internet Res", year="2017", month="Nov", day="06", volume="19", number="11", pages="e370", keywords="human influenza", keywords="classification and regression trees", keywords="nowcasts", keywords="infodemiology", keywords="infoveillance", keywords="surveillance", abstract="Background: Limiting the adverse effects of seasonal influenza outbreaks at state or city level requires close monitoring of localized outbreaks and reliable forecasts of their progression. Whereas forecasting models for influenza or influenza-like illness (ILI) are becoming increasingly available, their applicability to localized outbreaks is limited by the nonavailability of real-time observations of the current outbreak state at local scales. Surveillance data collected by various health departments are widely accepted as the reference standard for estimating the state of outbreaks, and in the absence of surveillance data, nowcast proxies built using Web-based activities such as search engine queries, tweets, and access of health-related webpages can be useful. Nowcast estimates of state and municipal ILI were previously published by Google Flu Trends (GFT); however, validations of these estimates were seldom reported. Objective: The aim of this study was to develop and validate models to nowcast ILI at subregional geographic scales. Methods: We built nowcast models based on autoregressive (autoregressive integrated moving average; ARIMA) and supervised regression methods (Random forests) at the US state level using regional weighted ILI and Web-based search activity derived from Google's Extended Trends application programming interface. We validated the performance of these methods using actual surveillance data for the 50 states across six seasons. We also built state-level nowcast models using state-level estimates of ILI and compared the accuracy of these estimates with the estimates of the regional models extrapolated to the state level and with the nowcast estimates published by GFT. Results: Models built using regional ILI extrapolated to state level had a median correlation of 0.84 (interquartile range: 0.74-0.91) and a median root mean square error (RMSE) of 1.01 (IQR: 0.74-1.50), with noticeable variability across seasons and by state population size. Model forms that hypothesize the availability of timely state-level surveillance data show significantly lower errors of 0.83 (0.55-0.23). Compared with GFT, the latter model forms have lower errors but also lower correlation. Conclusions: These results suggest that the proposed methods may be an alternative to the discontinued GFT and that further improvements in the quality of subregional nowcasts may require increased access to more finely resolved surveillance data. ", doi="10.2196/jmir.7486", url="http://www.jmir.org/2017/11/e370/", url="http://www.ncbi.nlm.nih.gov/pubmed/29109069" } @Article{info:doi/10.2196/publichealth.7344, author="Brownstein, S. John and Chu, Shuyu and Marathe, Achla and Marathe, V. Madhav and Nguyen, T. Andre and Paolotti, Daniela and Perra, Nicola and Perrotta, Daniela and Santillana, Mauricio and Swarup, Samarth and Tizzoni, Michele and Vespignani, Alessandro and Vullikanti, S. Anil Kumar and Wilson, L. Mandy and Zhang, Qian", title="Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches", journal="JMIR Public Health Surveill", year="2017", month="Nov", day="01", volume="3", number="4", pages="e83", keywords="forecasting", keywords="disease surveillance", keywords="crowdsourcing", keywords="nonresponse bias", abstract="Background: Influenza outbreaks affect millions of people every year and its surveillance is usually carried out in developed countries through a network of sentinel doctors who report the weekly number of Influenza-like Illness cases observed among the visited patients. Monitoring and forecasting the evolution of these outbreaks supports decision makers in designing effective interventions and allocating resources to mitigate their impact. Objective: Describe the existing participatory surveillance approaches that have been used for modeling and forecasting of the seasonal influenza epidemic, and how they can help strengthen real-time epidemic science and provide a more rigorous understanding of epidemic conditions. Methods: We describe three different participatory surveillance systems, WISDM (Widely Internet Sourced Distributed Monitoring), Influenzanet and Flu Near You (FNY), and show how modeling and simulation can be or has been combined with participatory disease surveillance to: i) measure the non-response bias in a participatory surveillance sample using WISDM; and ii) nowcast and forecast influenza activity in different parts of the world (using Influenzanet and Flu Near You). Results: WISDM-based results measure the participatory and sample bias for three epidemic metrics i.e. attack rate, peak infection rate, and time-to-peak, and find the participatory bias to be the largest component of the total bias. The Influenzanet platform shows that digital participatory surveillance data combined with a realistic data-driven epidemiological model can provide both short-term and long-term forecasts of epidemic intensities, and the ground truth data lie within the 95 percent confidence intervals for most weeks. The statistical accuracy of the ensemble forecasts increase as the season progresses. The Flu Near You platform shows that participatory surveillance data provide accurate short-term flu activity forecasts and influenza activity predictions. The correlation of the HealthMap Flu Trends estimates with the observed CDC ILI rates is 0.99 for 2013-2015. Additional data sources lead to an error reduction of about 40\% when compared to the estimates of the model that only incorporates CDC historical information. Conclusions: While the advantages of participatory surveillance, compared to traditional surveillance, include its timeliness, lower costs, and broader reach, it is limited by a lack of control over the characteristics of the population sample. Modeling and simulation can help overcome this limitation as well as provide real-time and long-term forecasting of influenza activity in data-poor parts of the world. ", doi="10.2196/publichealth.7344", url="http://publichealth.jmir.org/2017/4/e83/", url="http://www.ncbi.nlm.nih.gov/pubmed/29092812" } @Article{info:doi/10.2196/publichealth.6810, author="Ip, KM Dennis and Lau, HY Eric and So, Chi Hau and Xiao, Jingyi and Lam, Kin Chi and Fang, J. Vicky and Tam, Hung Yat and Leung, M. Gabriel and Cowling, J. Benjamin", title="A Smart Card-Based Electronic School Absenteeism System for Influenza-Like Illness Surveillance in Hong Kong: Design, Implementation, and Feasibility Assessment", journal="JMIR Public Health Surveill", year="2017", month="Oct", day="06", volume="3", number="4", pages="e67", keywords="influenza", keywords="public health surveillance", keywords="school health", keywords="absenteeism", keywords="smart cards", abstract="Background: School-aged children have the highest incidence of respiratory virus infections each year, and transmission of respiratory viruses such as influenza virus can be a major concern in school settings. School absenteeism data have been employed as a component of influenza surveillance systems in some locations. Data timeliness and system acceptance remain as key determinants affecting the usefulness of a prospective surveillance system. Objective: The aim of this study was to assess the feasibility of implementing an electronic school absenteeism surveillance system using smart card--based technology for influenza-like illness (ILI) surveillance among a representative network of local primary and secondary schools in Hong Kong. Methods: We designed and implemented a surveillance system according to the Protocol for a Standardized information infrastructure for Pandemic and Emerging infectious disease Response (PROSPER). We employed an existing smart card--based education and school administration platform for data capture, customized the user interface, and used additional back end systems built for other downstream surveillance steps. We invited local schools to participate and collected absenteeism data by the implemented system. We compared temporal trend of the absenteeism data with data from existing community sentinel and laboratory surveillance data. Results: We designed and implemented an ILI surveillance system utilizing smart card--based attendance tracking approach for data capture. We implemented the surveillance system in a total of 107 schools (including 66 primary schools and 41 secondary schools), covering a total of 75,052 children. The system successfully captured information on absences for 2 consecutive academic years (2012-2013 and 2013-2014). The absenteeism data we collected from the system reflected ILI activity in the community, with an upsurge in disease activity detected up to 1 to 2 weeks preceding other existing surveillance systems. Conclusions: We designed and implemented a novel smart card technology--based school absenteeism surveillance system. Our study demonstrated the feasibility of building a large-scale surveillance system riding on a routinely adopted data collection approach and the use of simple system enhancement to minimize workload implication and enhance system acceptability. Data from this system have potential value in supplementing existing sentinel influenza surveillance for situational awareness of influenza activity in the community. ", doi="10.2196/publichealth.6810", url="http://publichealth.jmir.org/2017/4/e67/", url="http://www.ncbi.nlm.nih.gov/pubmed/28986338" } @Article{info:doi/10.2196/iproc.8686, author="Kim, Myeongchan and Yune, Sehyo and Han, Wook Hyun", title="Detecting Influenza Epidemics Using Self-reported Data Through Mobile App (FeverCoach)", journal="iproc", year="2017", month="Sep", day="22", volume="3", number="1", pages="e56", keywords="children", keywords="epidemics", keywords="health care", keywords="human influenza", keywords="Mobile health (mHealth)", abstract="Background: Timely forecast of influenza activity is critical for a public health system to prepare for an influenza epidemic and mitigate its burden. Currently, influenza surveillance relies on traditional data sources such as reports from health care providers, which lag behind real-time by several days to weeks. In an effort to reduce the time lag, internet search information, voluntary web-based records, and electronic health records have been suggested as the alternative data sources for influenza surveillance. However, low specificity, low rate of report, or privacy concerns limits the use of such data. Objective: FeverCoach mobile application provides tailored information to help caregivers manage a febrile child. Using the self-reported diagnosis data submitted to the app, we developed a new algorithm that accurately predicted the influenza trend in South Korea. Methods: Users of FeverCoach agreed to the use of de-identified data for research purposes. The app shows information about use of antipyretics and adjuvant way to relieve fever when users enter the child's age, sex, body temperature, and the duration of fever. Users can choose from the list of 21 candidate diseases including Influenza after a physician office visit. Additional information about the disease was provided following submission of the diagnosis. Public influenza-like illness (ILI) data was obtained from the Korea Centers for Disease Control and Prevention (KCDC) website. The data was collected from September 2016 to March 2017. Ordinary least squares linear regression was used to build a model using the data from the app to predict the influenza trend. To perform linear regression, we calculate logit(Pcdc) and logit(Papp) where logit(p) is natural log of p/(1-p), Pcdc is (ILI visit counts)/(total patient visit counts) and Papp is (Influenza report on FeverCoach)/(total diagnosis report on FeverCoach). Results: We collected 13,014 self-reported diagnoses. Of all users, 81\% of the children were under 5 years of age. The animated visualization of spatiotemporal diagnosis report is available online at https://www.youtube.com/watch?v=-8kDXz43gO8. Ordinary least square regression showed significant association between logit(Pcdc) and logit(Papp) (R2=0.860, P<.001). Using this regression model, we could detect an influenza epidemic 5 days before the 2016-2017 season's influenza epidemic alert by KCDC. Conclusions: We found that it is possible to predict influenza epidemics earlier than KCDC with a relatively small amoount data. Collection of specific and accurate data was made possible by targeting a well-defined population. ", doi="10.2196/iproc.8686", url="http://www.iproc.org/2017/1/e56/" } @Article{info:doi/10.2196/publichealth.7429, author="Koppeschaar, E. Carl and Colizza, Vittoria and Guerrisi, Caroline and Turbelin, Cl{\'e}ment and Duggan, Jim and Edmunds, John W. and Kjels{\o}, Charlotte and Mexia, Ricardo and Moreno, Yamir and Meloni, Sandro and Paolotti, Daniela and Perrotta, Daniela and van Straten, Edward and Franco, O. Ana", title="Influenzanet: Citizens Among 10 Countries Collaborating to Monitor Influenza in Europe", journal="JMIR Public Health Surveill", year="2017", month="Sep", day="19", volume="3", number="3", pages="e66", keywords="influenza", keywords="surveillance", keywords="Internet", keywords="vaccination", keywords="Europe", abstract="Background: The wide availability of the Internet and the growth of digital communication technologies have become an important tool for epidemiological studies and health surveillance. Influenzanet is a participatory surveillance system monitoring the incidence of influenza-like illness (ILI) in Europe since 2003. It is based on data provided by volunteers who self-report their symptoms via the Internet throughout the influenza season and currently involves 10 countries. Objective: In this paper, we describe the Influenzanet system and provide an overview of results from several analyses that have been performed with the collected data, which include participant representativeness analyses, data validation (comparing ILI incidence rates between Influenzanet and sentinel medical practice networks), identification of ILI risk factors, and influenza vaccine effectiveness (VE) studies previously published. Additionally, we present new VE analyses for the Netherlands, stratified by age and chronic illness and offer suggestions for further work and considerations on the continuity and sustainability of the participatory system. Methods: Influenzanet comprises country-specific websites where residents can register to become volunteers to support influenza surveillance and have access to influenza-related information. Participants are recruited through different communication channels. Following registration, volunteers submit an intake questionnaire with their postal code and sociodemographic and medical characteristics, after which they are invited to report their symptoms via a weekly electronic newsletter reminder. Several thousands of participants have been engaged yearly in Influenzanet, with over 36,000 volunteers in the 2015-16 season alone. Results: In summary, for some traits and in some countries (eg, influenza vaccination rates in the Netherlands), Influenzanet participants were representative of the general population. However, for other traits, they were not (eg, participants underrepresent the youngest and oldest age groups in 7 countries). The incidence of ILI in Influenzanet was found to be closely correlated although quantitatively higher than that obtained by the sentinel medical practice networks. Various risk factors for acquiring an ILI infection were identified. The VE studies performed with Influenzanet data suggest that this surveillance system could develop into a complementary tool to measure the effectiveness of the influenza vaccine, eventually in real time. Conclusions: Results from these analyses illustrate that Influenzanet has developed into a fast and flexible monitoring system that can complement the traditional influenza surveillance performed by sentinel medical practices. The uniformity of Influenzanet allows for direct comparison of ILI rates between countries. It also has the important advantage of yielding individual data, which can be used to identify risk factors. The way in which the Influenzanet system is constructed allows the collection of data that could be extended beyond those of ILI cases to monitor pandemic influenza and other common or emerging diseases. ", doi="10.2196/publichealth.7429", url="http://publichealth.jmir.org/2017/3/e66/", url="http://www.ncbi.nlm.nih.gov/pubmed/28928112" } @Article{info:doi/10.2196/jmir.7393, author="Kagashe, Ireneus and Yan, Zhijun and Suheryani, Imran", title="Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data", journal="J Med Internet Res", year="2017", month="Sep", day="12", volume="19", number="9", pages="e315", keywords="machine learning", keywords="Twitter messaging", keywords="social media", keywords="disease outbreaks", keywords="influenza", keywords="public health surveillance", keywords="natural language processing", keywords="influenza vaccines", abstract="Background: Uptake of medicinal drugs (preventive or treatment) is among the approaches used to control disease outbreaks, and therefore, it is of vital importance to be aware of the counts or frequencies of most commonly used drugs and trending topics about these drugs from consumers for successful implementation of control measures. Traditional survey methods would have accomplished this study, but they are too costly in terms of resources needed, and they are subject to social desirability bias for topics discovery. Hence, there is a need to use alternative efficient means such as Twitter data and machine learning (ML) techniques. Objective: Using Twitter data, the aim of the study was to (1) provide a methodological extension for efficiently extracting widely consumed drugs during seasonal influenza and (2) extract topics from the tweets of these drugs and to infer how the insights provided by these topics can enhance seasonal influenza surveillance. Methods: From tweets collected during the 2012-13 flu season, we first identified tweets with mentions of drugs and then constructed an ML classifier using dependency words as features. The classifier was used to extract tweets that evidenced consumption of drugs, out of which we identified the mostly consumed drugs. Finally, we extracted trending topics from each of these widely used drugs' tweets using latent Dirichlet allocation (LDA). Results: Our proposed classifier obtained an F1 score of 0.82, which significantly outperformed the two benchmark classifiers (ie, P<.001 with the lexicon-based and P=.048 with the 1-gram term frequency [TF]). The classifier extracted 40,428 tweets that evidenced consumption of drugs out of 50,828 tweets with mentions of drugs. The most widely consumed drugs were influenza virus vaccines that had around 76.95\% (31,111/40,428) share of the total; other notable drugs were Theraflu, DayQuil, NyQuil, vitamins, acetaminophen, and oseltamivir. The topics of each of these drugs exhibited common themes or experiences from people who have consumed these drugs. Among these were the enabling and deterrent factors to influenza drugs uptake, which are keys to mitigating the severity of seasonal influenza outbreaks. Conclusions: The study results showed the feasibility of using tweets of widely consumed drugs to enhance seasonal influenza surveillance in lieu of the traditional or conventional surveillance approaches. Public health officials and other stakeholders can benefit from the findings of this study, especially in enhancing strategies for mitigating the severity of seasonal influenza outbreaks. The proposed methods can be extended to the outbreaks of other diseases. ", doi="10.2196/jmir.7393", url="http://www.jmir.org/2017/9/e315/", url="http://www.ncbi.nlm.nih.gov/pubmed/28899847" } @Article{info:doi/10.5210/ojphi.v9i2.8004, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2017", volume="9", number="2", pages="e8004", doi="10.5210/ojphi.v9i2.8004", url="", url="http://www.ncbi.nlm.nih.gov/pubmed/29026453" } @Article{info:doi/10.2196/publichealth.6648, author="Rosenthal, Mariana and Anderson, Katey and Tengelsen, Leslie and Carter, Kris and Hahn, Christine and Ball, Christopher", title="Evaluation of Sampling Recommendations From the Influenza Virologic Surveillance Right Size Roadmap for Idaho", journal="JMIR Public Health Surveill", year="2017", month="Aug", day="24", volume="3", number="3", pages="e57", keywords="influenza", keywords="sample size", keywords="public health surveillance", abstract="Background: The Right Size Roadmap was developed by the Association of Public Health Laboratories and the Centers for Disease Control and Prevention to improve influenza virologic surveillance efficiency. Guidelines were provided to state health departments regarding representativeness and statistical estimates of specimen numbers needed for seasonal influenza situational awareness, rare or novel influenza virus detection, and rare or novel influenza virus investigation. Objective: The aim of this study was to compare Roadmap sampling recommendations with Idaho's influenza virologic surveillance to determine implementation feasibility. Methods: We calculated the proportion of medically attended influenza-like illness (MA-ILI) from Idaho's influenza-like illness surveillance among outpatients during October 2008 to May 2014, applied data to Roadmap-provided sample size calculators, and compared calculations with actual numbers of specimens tested for influenza by the Idaho Bureau of Laboratories (IBL). We assessed representativeness among patients' tested specimens to census estimates by age, sex, and health district residence. Results: Among outpatients surveilled, Idaho's mean annual proportion of MA-ILI was 2.30\% (20,834/905,818) during a 5-year period. Thus, according to Roadmap recommendations, Idaho needs to collect 128 specimens from MA-ILI patients/week for situational awareness, 1496 influenza-positive specimens/week for detection of a rare or novel influenza virus at 0.2\% prevalence, and after detection, 478 specimens/week to confirm true prevalence is ?2\% of influenza-positive samples. The mean number of respiratory specimens Idaho tested for influenza/week, excluding the 2009-2010 influenza season, ranged from 6 to 24. Various influenza virus types and subtypes were collected and specimen submission sources were representative in terms of geographic distribution, patient age range and sex, and disease severity. Conclusions: Insufficient numbers of respiratory specimens are submitted to IBL for influenza laboratory testing. Increased specimen submission would facilitate meeting Roadmap sample size recommendations. ", doi="10.2196/publichealth.6648", url="http://publichealth.jmir.org/2017/3/e57/", url="http://www.ncbi.nlm.nih.gov/pubmed/28838883" } @Article{info:doi/10.2196/jmir.7101, author="Spreco, Armin and Eriksson, Olle and Dahlstr{\"o}m, {\"O}rjan and Cowling, John Benjamin and Timpka, Toomas", title="Integrated Detection and Prediction of Influenza Activity for Real-Time Surveillance: Algorithm Design", journal="J Med Internet Res", year="2017", month="Jun", day="15", volume="19", number="6", pages="e211", keywords="human influenza", keywords="algorithms", keywords="epidemiological surveillance", keywords="public health surveillance", keywords="evaluation research", keywords="epidemiological methods", abstract="Background: Influenza is a viral respiratory disease capable of causing epidemics that represent a threat to communities worldwide. The rapidly growing availability of electronic ``big data'' from diagnostic and prediagnostic sources in health care and public health settings permits advance of a new generation of methods for local detection and prediction of winter influenza seasons and influenza pandemics. Objective: The aim of this study was to present a method for integrated detection and prediction of influenza virus activity in local settings using electronically available surveillance data and to evaluate its performance by retrospective application on authentic data from a Swedish county. Methods: An integrated detection and prediction method was formally defined based on a design rationale for influenza detection and prediction methods adapted for local surveillance. The novel method was retrospectively applied on data from the winter influenza season 2008-09 in a Swedish county (population 445,000). Outcome data represented individuals who met a clinical case definition for influenza (based on International Classification of Diseases version 10 [ICD-10] codes) from an electronic health data repository. Information from calls to a telenursing service in the county was used as syndromic data source. Results: The novel integrated detection and prediction method is based on nonmechanistic statistical models and is designed for integration in local health information systems. The method is divided into separate modules for detection and prediction of local influenza virus activity. The function of the detection module is to alert for an upcoming period of increased load of influenza cases on local health care (using influenza-diagnosis data), whereas the function of the prediction module is to predict the timing of the activity peak (using syndromic data) and its intensity (using influenza-diagnosis data). For detection modeling, exponential regression was used based on the assumption that the beginning of a winter influenza season has an exponential growth of infected individuals. For prediction modeling, linear regression was applied on 7-day periods at the time in order to find the peak timing, whereas a derivate of a normal distribution density function was used to find the peak intensity. We found that the integrated detection and prediction method detected the 2008-09 winter influenza season on its starting day (optimal timeliness 0 days), whereas the predicted peak was estimated to occur 7 days ahead of the factual peak and the predicted peak intensity was estimated to be 26\% lower than the factual intensity (6.3 compared with 8.5 influenza-diagnosis cases/100,000). Conclusions: Our detection and prediction method is one of the first integrated methods specifically designed for local application on influenza data electronically available for surveillance. The performance of the method in a retrospective study indicates that further prospective evaluations of the methods are justified. ", doi="10.2196/jmir.7101", url="http://www.jmir.org/2017/6/e211/", url="http://www.ncbi.nlm.nih.gov/pubmed/28619700" } @Article{info:doi/10.5210/ojphi.v9i1.7592, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2017", volume="9", number="1", pages="e7592", doi="10.5210/ojphi.v9i1.7592", url="" } @Article{info:doi/10.5210/ojphi.v9i1.7603, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2017", volume="9", number="1", pages="e7603", doi="10.5210/ojphi.v9i1.7603", url="" } @Article{info:doi/10.5210/ojphi.v9i1.7629, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2017", volume="9", number="1", pages="e7629", doi="10.5210/ojphi.v9i1.7629", url="" } @Article{info:doi/10.5210/ojphi.v9i1.7636, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2017", volume="9", number="1", pages="e7636", doi="10.5210/ojphi.v9i1.7636", url="" } @Article{info:doi/10.5210/ojphi.v9i1.7656, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2017", volume="9", number="1", pages="e7656", doi="10.5210/ojphi.v9i1.7656", url="" } @Article{info:doi/10.5210/ojphi.v9i1.7671, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2017", volume="9", number="1", pages="e7671", doi="10.5210/ojphi.v9i1.7671", url="" } @Article{info:doi/10.5210/ojphi.v9i1.7683, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2017", volume="9", number="1", pages="e7683", doi="10.5210/ojphi.v9i1.7683", url="" } @Article{info:doi/10.5210/ojphi.v9i1.7689, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2017", volume="9", number="1", pages="e7689", doi="10.5210/ojphi.v9i1.7689", url="" } @Article{info:doi/10.5210/ojphi.v9i1.7701, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2017", volume="9", number="1", pages="e7701", doi="10.5210/ojphi.v9i1.7701", url="" } @Article{info:doi/10.5210/ojphi.v9i1.7703, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2017", volume="9", number="1", pages="e7703", doi="10.5210/ojphi.v9i1.7703", url="" } @Article{info:doi/10.5210/ojphi.v9i1.7704, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2017", volume="9", number="1", pages="e7704", doi="10.5210/ojphi.v9i1.7704", url="" } @Article{info:doi/10.5210/ojphi.v9i1.7738, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2017", volume="9", number="1", pages="e7738", doi="10.5210/ojphi.v9i1.7738", url="" } @Article{info:doi/10.5210/ojphi.v9i1.7739, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2017", volume="9", number="1", pages="e7739", doi="10.5210/ojphi.v9i1.7739", url="" } @Article{info:doi/10.5210/ojphi.v9i1.7749, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2017", volume="9", number="1", pages="e7749", doi="10.5210/ojphi.v9i1.7749", url="" } @Article{info:doi/10.5210/ojphi.v9i1.7755, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2017", volume="9", number="1", pages="e7755", doi="10.5210/ojphi.v9i1.7755", url="" } @Article{info:doi/10.5210/ojphi.v9i1.7761, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2017", volume="9", number="1", pages="e7761", doi="10.5210/ojphi.v9i1.7761", url="" } @Article{info:doi/10.5210/ojphi.v9i1.7768, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2017", volume="9", number="1", pages="e7768", doi="10.5210/ojphi.v9i1.7768", url="" } @Article{info:doi/10.5210/ojphi.v8i3.7011, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2016", volume="8", number="3", pages="e7011", doi="10.5210/ojphi.v8i3.7011", url="", url="http://www.ncbi.nlm.nih.gov/pubmed/28210419" } @Article{info:doi/10.2196/publichealth.5901, author="Sharpe, Danielle J. and Hopkins, S. Richard and Cook, L. Robert and Striley, W. Catherine", title="Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A Comparative Analysis", journal="JMIR Public Health Surveill", year="2016", month="Oct", day="20", volume="2", number="2", pages="e161", keywords="Internet", keywords="social media", keywords="Bayes theorem", keywords="public health surveillance", keywords="influenza, human", abstract="Background: Traditional influenza surveillance relies on influenza-like illness (ILI) syndrome that is reported by health care providers. It primarily captures individuals who seek medical care and misses those who do not. Recently, Web-based data sources have been studied for application to public health surveillance, as there is a growing number of people who search, post, and tweet about their illnesses before seeking medical care. Existing research has shown some promise of using data from Google, Twitter, and Wikipedia to complement traditional surveillance for ILI. However, past studies have evaluated these Web-based sources individually or dually without comparing all 3 of them, and it would be beneficial to know which of the Web-based sources performs best in order to be considered to complement traditional methods. Objective: The objective of this study is to comparatively analyze Google, Twitter, and Wikipedia by examining which best corresponds with Centers for Disease Control and Prevention (CDC) ILI data. It was hypothesized that Wikipedia will best correspond with CDC ILI data as previous research found it to be least influenced by high media coverage in comparison with Google and Twitter. Methods: Publicly available, deidentified data were collected from the CDC, Google Flu Trends, HealthTweets, and Wikipedia for the 2012-2015 influenza seasons. Bayesian change point analysis was used to detect seasonal changes, or change points, in each of the data sources. Change points in Google, Twitter, and Wikipedia that occurred during the exact week, 1 preceding week, or 1 week after the CDC's change points were compared with the CDC data as the gold standard. All analyses were conducted using the R package ``bcp'' version 4.0.0 in RStudio version 0.99.484 (RStudio Inc). In addition, sensitivity and positive predictive values (PPV) were calculated for Google, Twitter, and Wikipedia. Results: During the 2012-2015 influenza seasons, a high sensitivity of 92\% was found for Google, whereas the PPV for Google was 85\%. A low sensitivity of 50\% was calculated for Twitter; a low PPV of 43\% was found for Twitter also. Wikipedia had the lowest sensitivity of 33\% and lowest PPV of 40\%. Conclusions: Of the 3 Web-based sources, Google had the best combination of sensitivity and PPV in detecting Bayesian change points in influenza-related data streams. Findings demonstrated that change points in Google, Twitter, and Wikipedia data occasionally aligned well with change points captured in CDC ILI data, yet these sources did not detect all changes in CDC data and should be further studied and developed. ", doi="10.2196/publichealth.5901", url="http://publichealth.jmir.org/2016/2/e161/", url="http://www.ncbi.nlm.nih.gov/pubmed/27765731" } @Article{info:doi/10.2196/jmir.4955, author="Woo, Hyekyung and Cho, Youngtae and Shim, Eunyoung and Lee, Jong-Koo and Lee, Chang-Gun and Kim, Hwan Seong", title="Estimating Influenza Outbreaks Using Both Search Engine Query Data and Social Media Data in South Korea", journal="J Med Internet Res", year="2016", month="Jul", day="04", volume="18", number="7", pages="e177", keywords="influenza", keywords="surveillance", keywords="population surveillance", keywords="infodemiology", keywords="infoveillance", keywords="Internet search", keywords="query", keywords="social media", keywords="big data", keywords="forecasting", keywords="epidemiology", keywords="early response", abstract="Background: As suggested as early as in 2006, logs of queries submitted to search engines seeking information could be a source for detection of emerging influenza epidemics if changes in the volume of search queries are monitored (infodemiology). However, selecting queries that are most likely to be associated with influenza epidemics is a particular challenge when it comes to generating better predictions. Objective: In this study, we describe a methodological extension for detecting influenza outbreaks using search query data; we provide a new approach for query selection through the exploration of contextual information gleaned from social media data. Additionally, we evaluate whether it is possible to use these queries for monitoring and predicting influenza epidemics in South Korea. Methods: Our study was based on freely available weekly influenza incidence data and query data originating from the search engine on the Korean website Daum between April 3, 2011 and April 5, 2014. To select queries related to influenza epidemics, several approaches were applied: (1) exploring influenza-related words in social media data, (2) identifying the chief concerns related to influenza, and (3) using Web query recommendations. Optimal feature selection by least absolute shrinkage and selection operator (Lasso) and support vector machine for regression (SVR) were used to construct a model predicting influenza epidemics. Results: In total, 146 queries related to influenza were generated through our initial query selection approach. A considerable proportion of optimal features for final models were derived from queries with reference to the social media data. The SVR model performed well: the prediction values were highly correlated with the recent observed influenza-like illness (r=.956; P<.001) and virological incidence rate (r=.963; P<.001). Conclusions: These results demonstrate the feasibility of using search queries to enhance influenza surveillance in South Korea. In addition, an approach for query selection using social media data seems ideal for supporting influenza surveillance based on search query data. ", doi="10.2196/jmir.4955", url="http://www.jmir.org/2016/7/e177/", url="http://www.ncbi.nlm.nih.gov/pubmed/27377323" } @Article{info:doi/10.2196/jmir.5585, author="Klembczyk, Jeffrey Joseph and Jalalpour, Mehdi and Levin, Scott and Washington, E. Raynard and Pines, M. Jesse and Rothman, E. Richard and Dugas, Freyer Andrea", title="Google Flu Trends Spatial Variability Validated Against Emergency Department Influenza-Related Visits", journal="J Med Internet Res", year="2016", month="Jun", day="28", volume="18", number="6", pages="e175", keywords="influenza", keywords="surveillance", keywords="emergency department", keywords="google flu trends", keywords="infoveillance", abstract="Background: Influenza is a deadly and costly public health problem. Variations in its seasonal patterns cause dangerous surges in emergency department (ED) patient volume. Google Flu Trends (GFT) can provide faster influenza surveillance information than traditional CDC methods, potentially leading to improved public health preparedness. GFT has been found to correlate well with reported influenza and to improve influenza prediction models. However, previous validation studies have focused on isolated clinical locations. Objective: The purpose of the study was to measure GFT surveillance effectiveness by correlating GFT with influenza-related ED visits in 19 US cities across seven influenza seasons, and to explore which city characteristics lead to better or worse GFT effectiveness. Methods: Using Healthcare Cost and Utilization Project data, we collected weekly counts of ED visits for all patients with diagnosis (International Statistical Classification of Diseases 9) codes for influenza-related visits from 2005-2011 in 19 different US cities. We measured the correlation between weekly volume of GFT searches and influenza-related ED visits (ie, GFT ED surveillance effectiveness) per city. We evaluated the relationship between 15 publically available city indicators (11 sociodemographic, two health care utilization, and two climate) and GFT surveillance effectiveness using univariate linear regression. Results: Correlation between city-level GFT and influenza-related ED visits had a median of .84, ranging from .67 to .93 across 19 cities. Temporal variability was observed, with median correlation ranging from .78 in 2009 to .94 in 2005. City indicators significantly associated (P<.10) with improved GFT surveillance include higher proportion of female population, higher proportion with Medicare coverage, higher ED visits per capita, and lower socioeconomic status. Conclusions: GFT is strongly correlated with ED influenza-related visits at the city level, but unexplained variation over geographic location and time limits its utility as standalone surveillance. GFT is likely most useful as an early signal used in conjunction with other more comprehensive surveillance techniques. City indicators associated with improved GFT surveillance provide some insight into the variability of GFT effectiveness. For example, populations with lower socioeconomic status may have a greater tendency to initially turn to the Internet for health questions, thus leading to increased GFT effectiveness. GFT has the potential to provide valuable information to ED providers for patient care and to administrators for ED surge preparedness. ", doi="10.2196/jmir.5585", url="http://www.jmir.org/2016/6/e175/", url="http://www.ncbi.nlm.nih.gov/pubmed/27354313" } @Article{info:doi/10.2196/resprot.5478, author="Cutrona, L. Sarah and Sreedhara, Meera and Goff, L. Sarah and Fisher, D. Lloyd and Preusse, Peggy and Jackson, Madeline and Sundaresan, Devi and Garber, D. Lawrence and Mazor, M. Kathleen", title="Improving Rates of Influenza Vaccination Through Electronic Health Record Portal Messages, Interactive Voice Recognition Calls and Patient-Enabled Electronic Health Record Updates: Protocol for a Randomized Controlled Trial", journal="JMIR Res Protoc", year="2016", month="May", day="06", volume="5", number="2", pages="e56", keywords="electronic health records", keywords="influenza vaccines", keywords="clinical decision support", keywords="Internet", keywords="Telephone", keywords="Electronic Mail", keywords="Health Records, Personal", keywords="Medical Informatics Applications", abstract="Background: Clinical decision support (CDS), including computerized reminders for providers and patients, can improve health outcomes. CDS promoting influenza vaccination, delivered directly to patients via an electronic health record (EHR) patient portal and interactive voice recognition (IVR) calls, offers an innovative approach to improving patient care. Objective: To test the effectiveness of an EHR patient portal and IVR outreach to improve rates of influenza vaccination in a large multispecialty group practice in central Massachusetts. Methods: We describe a nonblinded, randomized controlled trial of EHR patient portal messages and IVR calls designed to promote influenza vaccination. In our preparatory phase, we conducted qualitative interviews with patients, providers, and staff to inform development of EHR portal messages with embedded questionnaires and IVR call scripts. We also provided practice-wide education on influenza vaccines to all physicians and staff members, including information on existing vaccine-specific EHR CDS. Outreach will target adult patients who remain unvaccinated for more than 2 months after the start of the influenza season. Using computer-generated randomization and a factorial design, we will assign 20,000 patients who are active users of electronic patient portals to one of the 4 study arms: (1) receipt of a portal message promoting influenza vaccines and offering online appointment scheduling; (2) receipt of an IVR call with similar content but without appointment facilitation; (3) both (1) and (2); or (4) neither (1) nor (2) (usual care). We will randomize patients without electronic portals (10,000 patients) to (1) receipt of IVR call or (2) usual care. Both portal messages and IVR calls promote influenza vaccine completion. Our primary outcome is percentage of eligible patients with influenza vaccines administered at our group practice during the 2014-15 influenza season. Both outreach methods also solicit patient self-report on influenza vaccinations completed outside the clinic or on barriers to influenza vaccination. Self-reported data from both outreach modes will be uploaded into the EHR to increase accuracy of existing provider-directed EHR CDS (vaccine alerts). Results: With our proposed sample size and using a factorial design, power calculations using baseline vaccination rate estimates indicated that 4286 participants per arm would give 80\% power to detect a 3\% improvement in influenza vaccination rates between groups ($\alpha$=.05; 2-sided). Intention-to-treat unadjusted chi-square analyses will be performed to assess the impact of portal messages, either alone or in combination with the IVR call, on influenza vaccination rates. The project was funded in January 2014. Patient enrollment for the project described here completed in December 2014. Data analysis is currently under way and first results are expected to be submitted for publication in 2016. Conclusions: If successful, this study's intervention may be adapted by other large health care organizations to increase vaccination rates among their eligible patients. ClinicalTrial: ClinicalTrials.gov NCT02266277; https://clinicaltrials.gov/ct2/show/NCT02266277 (Archived by WebCite at http://www.webcitation.org/6fbLviHLH). ", doi="10.2196/resprot.5478", url="http://www.researchprotocols.org/2016/2/e56/", url="http://www.ncbi.nlm.nih.gov/pubmed/27153752" } @Article{info:doi/10.5210/ojphi.v8i1.6412, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2016", volume="8", number="1", pages="e6412", doi="10.5210/ojphi.v8i1.6412", url="" } @Article{info:doi/10.5210/ojphi.v8i1.6434, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2016", volume="8", number="1", pages="e6434", doi="10.5210/ojphi.v8i1.6434", url="" } @Article{info:doi/10.5210/ojphi.v8i1.6437, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2016", volume="8", number="1", pages="e6437", doi="10.5210/ojphi.v8i1.6437", url="" } @Article{info:doi/10.5210/ojphi.v8i1.6438, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2016", volume="8", number="1", pages="e6438", doi="10.5210/ojphi.v8i1.6438", url="" } @Article{info:doi/10.5210/ojphi.v8i1.6449, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2016", volume="8", number="1", pages="e6449", doi="10.5210/ojphi.v8i1.6449", url="" } @Article{info:doi/10.5210/ojphi.v8i1.6450, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2016", volume="8", number="1", pages="e6450", doi="10.5210/ojphi.v8i1.6450", url="" } @Article{info:doi/10.5210/ojphi.v8i1.6463, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2016", volume="8", number="1", pages="e6463", doi="10.5210/ojphi.v8i1.6463", url="" } @Article{info:doi/10.5210/ojphi.v8i1.6468, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2016", volume="8", number="1", pages="e6468", doi="10.5210/ojphi.v8i1.6468", url="" } @Article{info:doi/10.5210/ojphi.v8i1.6484, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2016", volume="8", number="1", pages="e6484", doi="10.5210/ojphi.v8i1.6484", url="" } @Article{info:doi/10.5210/ojphi.v8i1.6488, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2016", volume="8", number="1", pages="e6488", doi="10.5210/ojphi.v8i1.6488", url="" } @Article{info:doi/10.5210/ojphi.v8i1.6493, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2016", volume="8", number="1", pages="e6493", doi="10.5210/ojphi.v8i1.6493", url="" } @Article{info:doi/10.5210/ojphi.v8i1.6496, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2016", volume="8", number="1", pages="e6496", doi="10.5210/ojphi.v8i1.6496", url="" } @Article{info:doi/10.5210/ojphi.v8i1.6518, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2016", volume="8", number="1", pages="e6518", doi="10.5210/ojphi.v8i1.6518", url="" } @Article{info:doi/10.5210/ojphi.v8i1.6557, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2016", volume="8", number="1", pages="e6557", doi="10.5210/ojphi.v8i1.6557", url="" } @Article{info:doi/10.5210/ojphi.v8i1.6567, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2016", volume="8", number="1", pages="e6567", doi="10.5210/ojphi.v8i1.6567", url="" } @Article{info:doi/10.5210/ojphi.v8i1.6575, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2016", volume="8", number="1", pages="e6575", doi="10.5210/ojphi.v8i1.6575", url="" } @Article{info:doi/10.5210/ojphi.v8i1.6581, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2016", volume="8", number="1", pages="e6581", doi="10.5210/ojphi.v8i1.6581", url="" } @Article{info:doi/10.2196/resprot.4331, author="El Rifay, S. Amira and Elabd, A. Mona and Abu Zeid, Dina and Gomaa, R. Mokhtar and Tang, Li and McKenzie, P. Pamela and Webby, J. Richard and Ali, A. Mohamed and Kayali, Ghazi", title="Household Transmission of Zoonotic Influenza Viruses in a Cohort of Egyptian Poultry Growers", journal="JMIR Res Protoc", year="2015", month="Jun", day="22", volume="4", number="2", pages="e74", keywords="influenza", keywords="avian", keywords="epidemiology", keywords="cohort", abstract="Background: The highly pathogenic avian influenza H5N1 viruses and the low pathogenic H9N2 viruses are enzootic in Egyptian poultry. Several cases of human infection with H5N1 were reported in Egypt. We previously determined that the seroprevalence of H5N1 antibodies in Egyptians exposed to poultry is 2.1\% (15/708), suggesting that mild or subclinical infections with this virus occur. We aim to measure the incidence of avian influenza infection in Egyptians exposed to poultry, study risk factors of infection, study the resulting immune response, study household transmission rates, and characterize the viruses causing infections. Objective: The objective of the study is to design a 7-year, prospective, household-based cohort investigation to determine incidence and household transmission of avian influenza viruses in humans exposed to poultry. Methods: At baseline, we will collect sera to measure antibodies against influenza A. Field nurses will visit enrolled subjects at least weekly to check for influenza-like illness symptoms and verify influenza infection by a point of care rapid test. From subjects with influenza infection and their household contacts, we will collect nasal swabs, throat swabs, and nasal washes to characterize the antigenic and genetic makeup of influenza viruses infecting humans. The nurse will also obtain 2x 3-ml blood samples, one for serology, and another for isolating peripheral blood mononuclear cells. Results: Results from this cohort will enhance our understanding of the transmission of avian influenza viruses to humans in a country where such viruses are enzootic. Conclusions: This may enhance public health efforts aimed at reducing this burden. ", doi="10.2196/resprot.4331", url="http://www.researchprotocols.org/2015/2/e74/", url="http://www.ncbi.nlm.nih.gov/pubmed/26099368" } @Article{info:doi/10.2196/publichealth.4472, author="Broniatowski, Andre David and Dredze, Mark and Paul, J. Michael and Dugas, Andrea", title="Using Social Media to Perform Local Influenza Surveillance in an Inner-City Hospital: A Retrospective Observational Study", journal="JMIR Public Health Surveill", year="2015", month="May", day="29", volume="1", number="1", pages="e5", keywords="Web mining", keywords="social computing", keywords="time series analysis", abstract="Background: Public health officials and policy makers in the United States expend significant resources at the national, state, county, and city levels to measure the rate of influenza infection. These individuals rely on influenza infection rate information to make important decisions during the course of an influenza season driving vaccination campaigns, clinical guidelines, and medical staffing. Web and social media data sources have emerged as attractive alternatives to supplement existing practices. While traditional surveillance methods take 1-2 weeks, and significant labor, to produce an infection estimate in each locale, web and social media data are available in near real-time for a broad range of locations. Objective: The objective of this study was to analyze the efficacy of flu surveillance from combining data from the websites Google Flu Trends and HealthTweets at the local level. We considered both emergency department influenza-like illness cases and laboratory-confirmed influenza cases for a single hospital in the City of Baltimore. Methods: This was a retrospective observational study comparing estimates of influenza activity of Google Flu Trends and Twitter to actual counts of individuals with laboratory-confirmed influenza, and counts of individuals presenting to the emergency department with influenza-like illness cases. Data were collected from November 20, 2011 through March 16, 2014. Each parameter was evaluated on the municipal, regional, and national scale. We examined the utility of social media data for tracking actual influenza infection at the municipal, state, and national levels. Specifically, we compared the efficacy of Twitter and Google Flu Trends data. Results: We found that municipal-level Twitter data was more effective than regional and national data when tracking actual influenza infection rates in a Baltimore inner-city hospital. When combined, national-level Twitter and Google Flu Trends data outperformed each data source individually. In addition, influenza-like illness data at all levels of geographic granularity were best predicted by national Google Flu Trends data. Conclusions: In order to overcome sensitivity to transient events, such as the news cycle, the best-fitting Google Flu Trends model relies on a 4-week moving average, suggesting that it may also be sacrificing sensitivity to transient fluctuations in influenza infection to achieve predictive power. Implications for influenza forecasting are discussed in this report. ", doi="10.2196/publichealth.4472", url="http://publichealth.jmir.org/2015/1/e5/", url="http://www.ncbi.nlm.nih.gov/pubmed/27014744" } @Article{info:doi/10.5210/ojphi.v7i1.5684, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2015", volume="7", number="1", pages="e5684", doi="10.5210/ojphi.v7i1.5684", url="" } @Article{info:doi/10.5210/ojphi.v7i1.5691, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2015", volume="7", number="1", pages="e5691", doi="10.5210/ojphi.v7i1.5691", url="" } @Article{info:doi/10.5210/ojphi.v7i1.5694, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2015", volume="7", number="1", pages="e5694", doi="10.5210/ojphi.v7i1.5694", url="" } @Article{info:doi/10.5210/ojphi.v7i1.5705, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2015", volume="7", number="1", pages="e5705", doi="10.5210/ojphi.v7i1.5705", url="" } @Article{info:doi/10.5210/ojphi.v7i1.5707, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2015", volume="7", number="1", pages="e5707", doi="10.5210/ojphi.v7i1.5707", url="" } @Article{info:doi/10.5210/ojphi.v7i1.5719, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2015", volume="7", number="1", pages="e5719", doi="10.5210/ojphi.v7i1.5719", url="" } @Article{info:doi/10.5210/ojphi.v7i1.5737, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2015", volume="7", number="1", pages="e5737", doi="10.5210/ojphi.v7i1.5737", url="" } @Article{info:doi/10.5210/ojphi.v7i1.5753, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2015", volume="7", number="1", pages="e5753", doi="10.5210/ojphi.v7i1.5753", url="" } @Article{info:doi/10.5210/ojphi.v7i1.5757, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2015", volume="7", number="1", pages="e5757", doi="10.5210/ojphi.v7i1.5757", url="" } @Article{info:doi/10.5210/ojphi.v7i1.5758, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2015", volume="7", number="1", pages="e5758", doi="10.5210/ojphi.v7i1.5758", url="" } @Article{info:doi/10.5210/ojphi.v7i1.5761, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2015", volume="7", number="1", pages="e5761", doi="10.5210/ojphi.v7i1.5761", url="" } @Article{info:doi/10.5210/ojphi.v7i1.5787, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2015", volume="7", number="1", pages="e5787", doi="10.5210/ojphi.v7i1.5787", url="" } @Article{info:doi/10.5210/ojphi.v7i1.5809, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2015", volume="7", number="1", pages="e5809", doi="10.5210/ojphi.v7i1.5809", url="" } @Article{info:doi/10.5210/ojphi.v7i1.5821, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2015", volume="7", number="1", pages="e5821", doi="10.5210/ojphi.v7i1.5821", url="" } @Article{info:doi/10.5210/ojphi.v7i1.5829, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2015", volume="7", number="1", pages="e5829", doi="10.5210/ojphi.v7i1.5829", url="" } @Article{info:doi/10.2196/mhealth.3394, author="Gu, Hua and Jiang, Zhenggang and Chen, Bin and Zhang, (Mandy) Jueman and Wang, Zhengting and Wang, Xinyi and Cai, Jian and Chen, Yongdi and Zheng, Dawei and Jiang, Jianmin", title="Knowledge, Attitudes, and Practices Regarding Avian Influenza A (H7N9) Among Mobile Phone Users: A Survey in Zhejiang Province, China", journal="JMIR mHealth uHealth", year="2015", month="Feb", day="04", volume="3", number="1", pages="e15", keywords="influenza A virus, subtype H7N9", keywords="knowledge", keywords="attitude", keywords="surveillance", abstract="Background: Understanding people's knowledge, attitudes, and practices (KAP) regarding a new infectious disease is crucial to the prevention and control of it. Human infection with avian influenza A (H7N9) was first identified on March 31, 2013 in China. Out of the total number of 134 cases confirmed from March to September 2013 in China, Zhejiang Province saw the greatest number (46 cases). Objective: This study employed a mobile Internet survey to assess KAP regarding H7N9 among mobile phone users in Zhejiang Province. This study intended to examine KAP by region and the association between sociodemographic variables and KAP. Methods: An anonymous questionnaire was designed by Zhejiang Provincial Center for Disease Control and Prevention (CDC). A cross-sectional survey was executed through a mobile Internet application platform of China Unicom in 5 regions in Zhejiang Province. Stratified and clustered sampling methods were applied and mobile phone users were invited to participate in the study voluntarily. Results: A total of 9582 eligible mobile phone users participated in the survey with a response rate of 1.92\% (9582/5,000,000). A total of 9105 valid responses (95.02\%) were included for statistical analysis. Generally, more than three-quarters of the participants had some basic knowledge of H7N9 and held the attitude recommended by the Zhejiang CDC toward eating cooked poultry (77.55\%, 7061/9105) and visiting a hospital at the occurrence of symptoms (78.51\%, 7148/9105). Approximately half of the participants worried about contracting H7N9, and took preventive practices recommended by the Zhejiang CDC. But only 14.29\% (1301/9105) of participants kept eating cooked poultry as usual. Although worry about H7N9 infection did not differ by region, Hangzhou saw the largest proportion of participants with knowledge of H7N9, which was probably because Hangzhou had the greatest number of H7N9 cases. KAP varied by some sociodemographic variables. Female participants were more likely to know about symptoms of H7N9 (OR 1.32, 95\% CI 1.08-1.61), to worry about contracting it (OR 1.15, 95\% CI 1.04-1.27), and to report their lives being influenced by it (OR 1.27, 95\% CI 1.15-1.41). They were also more likely to take the recommended precautions. Male participants and younger participants were less likely to comply with advocated protective practices. Conclusions: The results suggest that health education should be customized depending on sociodemographic variables to achieve more effective behavioral outcomes. ", doi="10.2196/mhealth.3394", url="http://mhealth.jmir.org/2015/1/e15/", url="http://www.ncbi.nlm.nih.gov/pubmed/25653213" } @Article{info:doi/10.2196/jmir.3680, author="Seo, Dong-Woo and Jo, Min-Woo and Sohn, Hwan Chang and Shin, Soo-Yong and Lee, JaeHo and Yu, Maengsoo and Kim, Young Won and Lim, Soo Kyoung and Lee, Sang-Il", title="Cumulative Query Method for Influenza Surveillance Using Search Engine Data", journal="J Med Internet Res", year="2014", month="Dec", day="16", volume="16", number="12", pages="e289", keywords="syndromic surveillance system", keywords="influenza", keywords="influenza-like illness", keywords="Google Flu Trends", keywords="Internet search", keywords="query", abstract="Background: Internet search queries have become an important data source in syndromic surveillance system. However, there is currently no syndromic surveillance system using Internet search query data in South Korea. Objectives: The objective of this study was to examine correlations between our cumulative query method and national influenza surveillance data. Methods: Our study was based on the local search engine, Daum (approximately 25\% market share), and influenza-like illness (ILI) data from the Korea Centers for Disease Control and Prevention. A quota sampling survey was conducted with 200 participants to obtain popular queries. We divided the study period into two sets: Set 1 (the 2009/10 epidemiological year for development set 1 and 2010/11 for validation set 1) and Set 2 (2010/11 for development Set 2 and 2011/12 for validation Set 2). Pearson's correlation coefficients were calculated between the Daum data and the ILI data for the development set. We selected the combined queries for which the correlation coefficients were .7 or higher and listed them in descending order. Then, we created a cumulative query method n representing the number of cumulative combined queries in descending order of the correlation coefficient. Results: In validation set 1, 13 cumulative query methods were applied, and 8 had higher correlation coefficients (min=.916, max=.943) than that of the highest single combined query. Further, 11 of 13 cumulative query methods had an r value of ?.7, but 4 of 13 combined queries had an r value of ?.7. In validation set 2, 8 of 15 cumulative query methods showed higher correlation coefficients (min=.975, max=.987) than that of the highest single combined query. All 15 cumulative query methods had an r value of ?.7, but 6 of 15 combined queries had an r value of ?.7. Conclusions: Cumulative query method showed relatively higher correlation with national influenza surveillance data than combined queries in the development and validation set. ", doi="10.2196/jmir.3680", url="http://www.jmir.org/2014/12/e289/", url="http://www.ncbi.nlm.nih.gov/pubmed/25517353" } @Article{info:doi/10.2196/jmir.3532, author="Aslam, A. Anosh{\'e} and Tsou, Ming-Hsiang and Spitzberg, H. Brian and An, Li and Gawron, Mark J. and Gupta, K. Dipak and Peddecord, Michael K. and Nagel, C. Anna and Allen, Christopher and Yang, Jiue-An and Lindsay, Suzanne", title="The Reliability of Tweets as a Supplementary Method of Seasonal Influenza Surveillance", journal="J Med Internet Res", year="2014", month="Nov", day="14", volume="16", number="11", pages="e250", keywords="Twitter", keywords="tweets", keywords="infoveillance", keywords="infodemiology", keywords="syndromic surveillance", keywords="influenza", keywords="Internet", abstract="Background: Existing influenza surveillance in the United States is focused on the collection of data from sentinel physicians and hospitals; however, the compilation and distribution of reports are usually delayed by up to 2 weeks. With the popularity of social media growing, the Internet is a source for syndromic surveillance due to the availability of large amounts of data. In this study, tweets, or posts of 140 characters or less, from the website Twitter were collected and analyzed for their potential as surveillance for seasonal influenza. Objective: There were three aims: (1) to improve the correlation of tweets to sentinel-provided influenza-like illness (ILI) rates by city through filtering and a machine-learning classifier, (2) to observe correlations of tweets for emergency department ILI rates by city, and (3) to explore correlations for tweets to laboratory-confirmed influenza cases in San Diego. Methods: Tweets containing the keyword ``flu'' were collected within a 17-mile radius from 11 US cities selected for population and availability of ILI data. At the end of the collection period, 159,802 tweets were used for correlation analyses with sentinel-provided ILI and emergency department ILI rates as reported by the corresponding city or county health department. Two separate methods were used to observe correlations between tweets and ILI rates: filtering the tweets by type (non-retweets, retweets, tweets with a URL, tweets without a URL), and the use of a machine-learning classifier that determined whether a tweet was ``valid'', or from a user who was likely ill with the flu. Results: Correlations varied by city but general trends were observed. Non-retweets and tweets without a URL had higher and more significant (P<.05) correlations than retweets and tweets with a URL. Correlations of tweets to emergency department ILI rates were higher than the correlations observed for sentinel-provided ILI for most of the cities. The machine-learning classifier yielded the highest correlations for many of the cities when using the sentinel-provided or emergency department ILI as well as the number of laboratory-confirmed influenza cases in San Diego. High correlation values (r=.93) with significance at P<.001 were observed for laboratory-confirmed influenza cases for most categories and tweets determined to be valid by the classifier. Conclusions: Compared to tweet analyses in the previous influenza season, this study demonstrated increased accuracy in using Twitter as a supplementary surveillance tool for influenza as better filtering and classification methods yielded higher correlations for the 2013-2014 influenza season than those found for tweets in the previous influenza season, where emergency department ILI rates were better correlated to tweets than sentinel-provided ILI rates. Further investigations in the field would require expansion with regard to the location that the tweets are collected from, as well as the availability of more ILI data. ", doi="10.2196/jmir.3532", url="http://www.jmir.org/2014/11/e250/", url="http://www.ncbi.nlm.nih.gov/pubmed/25406040" } @Article{info:doi/10.2196/jmir.3416, author="Nagar, Ruchit and Yuan, Qingyu and Freifeld, C. Clark and Santillana, Mauricio and Nojima, Aaron and Chunara, Rumi and Brownstein, S. John", title="A Case Study of the New York City 2012-2013 Influenza Season With Daily Geocoded Twitter Data From Temporal and Spatiotemporal Perspectives", journal="J Med Internet Res", year="2014", month="Oct", day="20", volume="16", number="10", pages="e236", keywords="influenza", keywords="Twitter", keywords="New York City", keywords="spatiotemporal", keywords="Google Flu Trends", keywords="infodemiology", keywords="mHealth", keywords="social media, natural language processing", keywords="medical informatics", abstract="Background: Twitter has shown some usefulness in predicting influenza cases on a weekly basis in multiple countries and on different geographic scales. Recently, Broniatowski and colleagues suggested Twitter's relevance at the city-level for New York City. Here, we look to dive deeper into the case of New York City by analyzing daily Twitter data from temporal and spatiotemporal perspectives. Also, through manual coding of all tweets, we look to gain qualitative insights that can help direct future automated searches. Objective: The intent of the study was first to validate the temporal predictive strength of daily Twitter data for influenza-like illness emergency department (ILI-ED) visits during the New York City 2012-2013 influenza season against other available and established datasets (Google search query, or GSQ), and second, to examine the spatial distribution and the spread of geocoded tweets as proxies for potential cases. Methods: From the Twitter Streaming API, 2972 tweets were collected in the New York City region matching the keywords ``flu'', ``influenza'', ``gripe'', and ``high fever''. The tweets were categorized according to the scheme developed by Lamb et al. A new fourth category was added as an evaluator guess for the probability of the subject(s) being sick to account for strength of confidence in the validity of the statement. Temporal correlations were made for tweets against daily ILI-ED visits and daily GSQ volume. The best models were used for linear regression for forecasting ILI visits. A weighted, retrospective Poisson model with SaTScan software (n=1484), and vector map were used for spatiotemporal analysis. Results: Infection-related tweets (R=.763) correlated better than GSQ time series (R=.683) for the same keywords and had a lower mean average percent error (8.4 vs 11.8) for ILI-ED visit prediction in January, the most volatile month of flu. SaTScan identified primary outbreak cluster of high-probability infection tweets with a 2.74 relative risk ratio compared to medium-probability infection tweets at P=.001 in Northern Brooklyn, in a radius that includes Barclay's Center and the Atlantic Avenue Terminal. Conclusions: While others have looked at weekly regional tweets, this study is the first to stress test Twitter for daily city-level data for New York City. Extraction of personal testimonies of infection-related tweets suggests Twitter's strength both qualitatively and quantitatively for ILI-ED prediction compared to alternative daily datasets mixed with awareness-based data such as GSQ. Additionally, granular Twitter data provide important spatiotemporal insights. A tweet vector-map may be useful for visualization of city-level spread when local gold standard data are otherwise unavailable. ", doi="10.2196/jmir.3416", url="http://www.jmir.org/2014/10/e236/", url="http://www.ncbi.nlm.nih.gov/pubmed/25331122" } @Article{info:doi/10.2196/jmir.3763, author="Mao, Chen and Wu, Xin-Yin and Fu, Xiao-Hong and Di, Meng-Yang and Yu, Yuan-Yuan and Yuan, Jin-Qiu and Yang, Zu-Yao and Tang, Jin-Ling", title="An Internet-Based Epidemiological Investigation of the Outbreak of H7N9 Avian Influenza A in China Since Early 2013", journal="J Med Internet Res", year="2014", month="Sep", day="25", volume="16", number="9", pages="e221", keywords="influenza A virus, H7N9 subtype", keywords="Internet", keywords="big data", keywords="disease outbreaks", keywords="epidemiology", abstract="Background: In early 2013, a new type of avian influenza, H7N9, emerged in China. It quickly became an issue of great public concern and a widely discussed topic on the Internet. A considerable volume of relevant information was made publicly available on the Internet through various sources. Objective: This study aimed to describe the outbreak of H7N9 in China based on data openly available on the Internet and to validate our investigation by comparing our findings with a well-conducted conventional field epidemiologic study. Methods: We searched publicly accessible Internet data on the H7N9 outbreak primarily from government and major mass media websites in China up to February 10, 2014. Two researchers independently extracted, compared, and confirmed the information of each confirmed H7N9 case using a self-designed data extraction form. We summarized the epidemiological and clinical characteristics of confirmed H7N9 cases and compared them with those from the field study. Results: According to our data updated until February 10, 2014, 334 confirmed H7N9 cases were identified. The median age was 58 years and 67.0\% (219/327) were males. Cases were reported in 15 regions in China. Five family clusters were found. Of the 16.8\% (56/334) of the cases with relevant data, 69.6\% (39/56) reported a history of exposure to animals. Of the 1751 persons with a close contact with a confirmed case, 0.6\% (11/1751) of them developed respiratory symptoms during the 7-day surveillance period. In the 97.9\% (327/334) of the cases with relevant data, 21.7\% (71/327) died, 20.8\% (68/327) were discharged from a hospital, and 57.5\% (188/327) were of uncertain status. We compared our findings before February 10, 2014 and those before December 1, 2013 with those from the conventional field study, which had the latter cutoff date of ours in data collection. Our study showed most epidemiological and clinical characteristics were similar to those in the field study, except for case fatality (71/327, 21.7\% for our data before February 10; 45/138, 32.6\% for our data before December 1; 47/139, 33.8\% for the field study), time from illness onset to first medical care (4 days, 3 days, and 1 day), and time from illness onset to death (16.5 days, 17 days, and 21 days). Conclusions: Findings from our Internet-based investigation were similar to those from the conventional field study in most epidemiological and clinical aspects of the outbreak. Importantly, publicly available Internet data are open to any interested researchers and can thus greatly facilitate the investigation and control of such outbreaks. With improved efforts for Internet data provision, Internet-based investigation has a great potential to become a quick, economical, novel approach to investigating sudden issues of great public concern that involve a relatively small number of cases like this H7N9 outbreak. ", doi="10.2196/jmir.3763", url="http://www.jmir.org/2014/9/e221/", url="http://www.ncbi.nlm.nih.gov/pubmed/25257217" } @Article{info:doi/10.2196/jmir.3099, author="Timpka, Toomas and Spreco, Armin and Dahlstr{\"o}m, {\"O}rjan and Eriksson, Olle and Gursky, Elin and Ekberg, Joakim and Blomqvist, Eva and Str{\"o}mgren, Magnus and Karlsson, David and Eriksson, Henrik and Nyce, James and Hinkula, Jorma and Holm, Einar", title="Performance of eHealth Data Sources in Local Influenza Surveillance:A 5-Year Open Cohort Study", journal="J Med Internet Res", year="2014", month="Apr", day="28", volume="16", number="4", pages="e116", keywords="influenza", keywords="infectious disease surveillance", keywords="Internet", keywords="eHealth", keywords="Google Flu Trends", keywords="telenursing call centers", keywords="website usage", keywords="open cohort design", keywords="public health", abstract="Background: There is abundant global interest in using syndromic data from population-wide health information systems---referred to as eHealth resources---to improve infectious disease surveillance. Recently, the necessity for these systems to achieve two potentially conflicting requirements has been emphasized. First, they must be evidence-based; second, they must be adjusted for the diversity of populations, lifestyles, and environments. Objective: The primary objective was to examine correlations between data from Google Flu Trends (GFT), computer-supported telenursing centers, health service websites, and influenza case rates during seasonal and pandemic influenza outbreaks. The secondary objective was to investigate associations between eHealth data, media coverage, and the interaction between circulating influenza strain(s) and the age-related population immunity. Methods: An open cohort design was used for a five-year study in a Swedish county (population 427,000). Syndromic eHealth data were collected from GFT, telenursing call centers, and local health service website visits at page level. Data on mass media coverage of influenza was collected from the major regional newspaper. The performance of eHealth data in surveillance was measured by correlation effect size and time lag to clinically diagnosed influenza cases. Results: Local media coverage data and influenza case rates showed correlations with large effect sizes only for the influenza A (A) pH1N1 outbreak in 2009 (r=.74, 95\% CI .42-.90; P<.001) and the severe seasonal A H3N2 outbreak in 2011-2012 (r=.79, 95\% CI .42-.93; P=.001), with media coverage preceding case rates with one week. Correlations between GFT and influenza case data showed large effect sizes for all outbreaks, the largest being the seasonal A H3N2 outbreak in 2008-2009 (r=.96, 95\% CI .88-.99; P<.001). The preceding time lag decreased from two weeks during the first outbreaks to one week from the 2009 A pH1N1 pandemic. Telenursing data and influenza case data showed correlations with large effect sizes for all outbreaks after the seasonal B and A H1 outbreak in 2007-2008, with a time lag decreasing from two weeks for the seasonal A H3N2 outbreak in 2008-2009 (r=.95, 95\% CI .82-.98; P<.001) to none for the A p H1N1 outbreak in 2009 (r=.84, 95\% CI .62-.94; P<.001). Large effect sizes were also observed between website visits and influenza case data. Conclusions: Correlations between the eHealth data and influenza case rates in a Swedish county showed large effect sizes throughout a five-year period, while the time lag between signals in eHealth data and influenza rates changed. Further research is needed on analytic methods for adjusting eHealth surveillance systems to shifts in media coverage and to variations in age-group related immunity between virus strains. The results can be used to inform the development of alert-generating eHealth surveillance systems that can be subject for prospective evaluations in routine public health practice. ", doi="10.2196/jmir.3099", url="http://www.jmir.org/2014/4/e116/", url="http://www.ncbi.nlm.nih.gov/pubmed/24776527" } @Article{info:doi/10.2196/jmir.3010, author="Bajardi, Paolo and Vespignani, Alessandro and Funk, Sebastian and Eames, TD Ken and Edmunds, John W. and Turbelin, Cl{\'e}ment and Debin, Marion and Colizza, Vittoria and Smallenburg, Ronald and Koppeschaar, E. Carl and Franco, O. Ana and Faustino, Vitor and Carnahan, Annasara and Rehn, Moa and Paolotti, Daniela", title="Determinants of Follow-Up Participation in the Internet-Based European Influenza Surveillance Platform Influenzanet", journal="J Med Internet Res", year="2014", month="Mar", day="10", volume="16", number="3", pages="e78", keywords="participatory surveillance", keywords="Internet", keywords="influenza", abstract="Background: ``Influenzanet'' is a network of Internet-based platforms aimed at collecting real-time data for influenza surveillance in several European countries. More than 30,000 European volunteers participate every year in the study, representing one of the largest existing Internet-based multicenter cohorts. Each week during the influenza season, participants are asked to report their symptoms (if any) along with a set of additional questions. Objective: Focusing on the first influenza season of 2011-12, when the Influenzanet system was completely harmonized within a common framework in Sweden, the United Kingdom, the Netherlands, Belgium, France, Italy, and Portugal, we investigated the propensity of users to regularly come back to the platform to provide information about their health status. Our purpose was to investigate demographic and behavioral factors associated with participation in follow-up. Methods: By means of a multilevel analysis, we evaluated the association between regular participation during the season and sociodemographic and behavioral characteristics as measured by a background questionnaire completed by participants on registration. Results: We found that lower participation in follow-up was associated with lower educational status (odds ratio [OR] 0.80, 95\% CI 0.75-0.85), smoking (OR 0.64, 95\% CI 0.59-0.70), younger age (OR ranging from 0.30, 95\% CI 0.26-0.33 to 0.70, 95\% CI 0.64-0.77), not being vaccinated against seasonal influenza (OR 0.77, 95\% CI 0.72-0.84), and living in a household with children (OR 0.69, 95\% CI 0.65-0.74). Most of these results hold when single countries are analyzed separately. Conclusions: Given the opportunistic enrollment of self-selected volunteers in the Influenzanet study, we have investigated how sociodemographic and behavioral characteristics may be associated with follow-up participation in the Influenzanet cohort. The study described in this paper shows that, overall, the most important determinants of participation are related to education and lifestyle: smoking, lower education level, younger age, people living with children, and people who have not been vaccinated against seasonal influenza tend to have a lower participation in follow-up. Despite the cross-country variation, the main findings are similar in the different national cohorts, and indeed the results are found to be valid also when performing a single-country analysis. Differences between countries do not seem to play a crucial role in determining the factors associated with participation in follow-up. ", doi="10.2196/jmir.3010", url="http://www.jmir.org/2014/3/e78/", url="http://www.ncbi.nlm.nih.gov/pubmed/24613818" } @Article{info:doi/10.5210/ojphi.v6i1.5046, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2014", volume="6", number="1", pages="e5046", doi="10.5210/ojphi.v6i1.5046", url="" } @Article{info:doi/10.5210/ojphi.v6i1.5078, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2014", volume="6", number="1", pages="e5078", doi="10.5210/ojphi.v6i1.5078", url="" } @Article{info:doi/10.5210/ojphi.v6i1.5102, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2014", volume="6", number="1", pages="e5102", doi="10.5210/ojphi.v6i1.5102", url="" } @Article{info:doi/10.5210/ojphi.v6i1.5105, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2014", volume="6", number="1", pages="e5105", doi="10.5210/ojphi.v6i1.5105", url="" } @Article{info:doi/10.5210/ojphi.v6i1.5106, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2014", volume="6", number="1", pages="e5106", doi="10.5210/ojphi.v6i1.5106", url="" } @Article{info:doi/10.5210/ojphi.v6i1.5120, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2014", volume="6", number="1", pages="e5120", doi="10.5210/ojphi.v6i1.5120", url="" } @Article{info:doi/10.5210/ojphi.v6i1.5121, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2014", volume="6", number="1", pages="e5121", doi="10.5210/ojphi.v6i1.5121", url="" } @Article{info:doi/10.5210/ojphi.v6i1.5149, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2014", volume="6", number="1", pages="e5149", doi="10.5210/ojphi.v6i1.5149", url="" } @Article{info:doi/10.5210/ojphi.v6i1.5179, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2014", volume="6", number="1", pages="e5179", doi="10.5210/ojphi.v6i1.5179", url="" } @Article{info:doi/10.5210/ojphi.v6i1.5180, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2014", volume="6", number="1", pages="e5180", doi="10.5210/ojphi.v6i1.5180", url="" } @Article{info:doi/10.5210/ojphi.v6i1.5187, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2014", volume="6", number="1", pages="e5187", doi="10.5210/ojphi.v6i1.5187", url="" } @Article{info:doi/10.5210/ojphi.v6i1.5188, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2014", volume="6", number="1", pages="e5188", doi="10.5210/ojphi.v6i1.5188", url="" } @Article{info:doi/10.5210/ojphi.v6i1.5189, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2014", volume="6", number="1", pages="e5189", doi="10.5210/ojphi.v6i1.5189", url="" } @Article{info:doi/10.5210/ojphi.v6i1.5016, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2014", volume="6", number="1", pages="e5016", doi="10.5210/ojphi.v6i1.5016", url="" } @Article{info:doi/10.2196/jmir.2911, author="Gu, Hua and Chen, Bin and Zhu, Honghong and Jiang, Tao and Wang, Xinyi and Chen, Lei and Jiang, Zhenggang and Zheng, Dawei and Jiang, Jianmin", title="Importance of Internet Surveillance in Public Health Emergency Control and Prevention: Evidence From a Digital Epidemiologic Study During Avian Influenza A H7N9 Outbreaks", journal="J Med Internet Res", year="2014", month="Jan", day="17", volume="16", number="1", pages="e20", keywords="influenza A virus, H7N9 subtype", keywords="Internet", keywords="surveillance", keywords="disease outbreak", abstract="Background: Outbreaks of human infection with a new avian influenza A H7N9 virus occurred in China in the spring of 2013. Control and prevention of a new human infectious disease outbreak can be strongly affected by public reaction and social impact through the Internet and social media. Objective: This study aimed to investigate the potential roles of Internet surveillance in control and prevention of the human H7N9 outbreaks. Methods: Official data for the human H7N9 outbreaks were collected via the China National Health and Family Planning Committee website from March 31 to April 24, 2013. We obtained daily posted and forwarded number of blogs for the keyword ``H7N9'' from Sina microblog website and a daily Baidu Attention Index (BAI) from Baidu website, which reflected public attention to the outbreak. Rumors identified and confirmed by the authorities were collected from Baidu search engine. Results: Both daily posted and forwarded number and BAI for keyword H7N9 increased quickly during the first 3 days of the outbreaks and remained at a high level for 5 days. The total daily posted and forwarded number for H7N9 on Sina microblog peaked at 850,000 on April 3, from zero blogs before March 31, increasing to 97,726 on April 1 and to 370,607 on April 2, and remaining above 500,000 from April 5-8 before declining to 208,524 on April 12. The total daily BAI showed a similar pattern of change to the total daily posted and forwarded number over time from March 31 to April 12. When the outbreak locations spread, especially into other areas of the same province/city and the capital, Beijing, daily posted and forwarded number and BAI increased again to a peak at 368,500 and 116,911, respectively. The median daily BAI during the studied 25 days was significantly higher among the 7 provinces/cities with reported human H7N9 cases than the 2 provinces without any cases (P<.001). So were the median daily posted and forwarded number and daily BAI in each province/city except Anhui province. We retrieved a total of 32 confirmed rumors spread across 19 provinces/cities in China. In all, 84\% (27/32) of rumors were disseminated and transmitted by social media. Conclusions: The first 3 days of an epidemic is a critical period for the authorities to take appropriate action through Internet surveillance to prevent and control the epidemic, including preparation of personnel, technology, and other resources; information release; collection of public opinion and reaction; and clarification, prevention, and control of rumors. Internet surveillance can be used as an efficient and economical tool to prevent and control public health emergencies, such as H7N9 outbreaks. ", doi="10.2196/jmir.2911", url="http://www.jmir.org/2014/1/e20/", url="http://www.ncbi.nlm.nih.gov/pubmed/24440770" } @Article{info:doi/10.2196/jmir.2705, author="Nagel, C. Anna and Tsou, Ming-Hsiang and Spitzberg, H. Brian and An, Li and Gawron, Mark J. and Gupta, K. Dipak and Yang, Jiue-An and Han, Su and Peddecord, Michael K. and Lindsay, Suzanne and Sawyer, H. Mark", title="The Complex Relationship of Realspace Events and Messages in Cyberspace: Case Study of Influenza and Pertussis Using Tweets", journal="J Med Internet Res", year="2013", month="Oct", day="26", volume="15", number="10", pages="e237", keywords="Twitter", keywords="infoveillance", keywords="infodemiology", keywords="cyberspace", keywords="syndromic surveillance", keywords="influenza", keywords="pertussis", keywords="whooping cough", abstract="Background: Surveillance plays a vital role in disease detection, but traditional methods of collecting patient data, reporting to health officials, and compiling reports are costly and time consuming. In recent years, syndromic surveillance tools have expanded and researchers are able to exploit the vast amount of data available in real time on the Internet at minimal cost. Many data sources for infoveillance exist, but this study focuses on status updates (tweets) from the Twitter microblogging website. Objective: The aim of this study was to explore the interaction between cyberspace message activity, measured by keyword-specific tweets, and real world occurrences of influenza and pertussis. Tweets were aggregated by week and compared to weekly influenza-like illness (ILI) and weekly pertussis incidence. The potential effect of tweet type was analyzed by categorizing tweets into 4 categories: nonretweets, retweets, tweets with a URL Web address, and tweets without a URL Web address. Methods: Tweets were collected within a 17-mile radius of 11 US cities chosen on the basis of population size and the availability of disease data. Influenza analysis involved all 11 cities. Pertussis analysis was based on the 2 cities nearest to the Washington State pertussis outbreak (Seattle, WA and Portland, OR). Tweet collection resulted in 161,821 flu, 6174 influenza, 160 pertussis, and 1167 whooping cough tweets. The correlation coefficients between tweets or subgroups of tweets and disease occurrence were calculated and trends were presented graphically. Results: Correlations between weekly aggregated tweets and disease occurrence varied greatly, but were relatively strong in some areas. In general, correlation coefficients were stronger in the flu analysis compared to the pertussis analysis. Within each analysis, flu tweets were more strongly correlated with ILI rates than influenza tweets, and whooping cough tweets correlated more strongly with pertussis incidence than pertussis tweets. Nonretweets correlated more with disease occurrence than retweets, and tweets without a URL Web address correlated better with actual incidence than those with a URL Web address primarily for the flu tweets. Conclusions: This study demonstrates that not only does keyword choice play an important role in how well tweets correlate with disease occurrence, but that the subgroup of tweets used for analysis is also important. This exploratory work shows potential in the use of tweets for infoveillance, but continued efforts are needed to further refine research methods in this field. ", doi="10.2196/jmir.2705", url="http://www.jmir.org/2013/10/e237/", url="http://www.ncbi.nlm.nih.gov/pubmed/24158773" } @Article{info:doi/10.5210/ojphi.v5i1.4446, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2013", volume="5", number="1", pages="e4446", doi="10.5210/ojphi.v5i1.4446", url="" } @Article{info:doi/10.5210/ojphi.v5i1.4567, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2013", volume="5", number="1", pages="e4567", doi="10.5210/ojphi.v5i1.4567", url="" } @Article{info:doi/10.5210/ojphi.v5i1.4594, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2013", volume="5", number="1", pages="e4594", doi="10.5210/ojphi.v5i1.4594", url="" } @Article{info:doi/10.5210/ojphi.v5i1.4605, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2013", volume="5", number="1", pages="e4605", doi="10.5210/ojphi.v5i1.4605", url="" } @Article{ref1, url="" } @Article{info:doi/10.5210/ojphi.v5i1.4403, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2013", volume="5", number="1", pages="e4403", doi="10.5210/ojphi.v5i1.4403", url="" } @Article{info:doi/10.5210/ojphi.v5i1.4406, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2013", volume="5", number="1", pages="e4406", doi="10.5210/ojphi.v5i1.4406", url="" } @Article{info:doi/10.5210/ojphi.v5i1.4415, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2013", volume="5", number="1", pages="e4415", doi="10.5210/ojphi.v5i1.4415", url="" } @Article{info:doi/10.5210/ojphi.v5i1.4456, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2013", volume="5", number="1", pages="e4456", doi="10.5210/ojphi.v5i1.4456", url="" } @Article{info:doi/10.5210/ojphi.v5i1.4461, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2013", volume="5", number="1", pages="e4461", doi="10.5210/ojphi.v5i1.4461", url="" } @Article{info:doi/10.5210/ojphi.v5i1.4470, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2013", volume="5", number="1", pages="e4470", doi="10.5210/ojphi.v5i1.4470", url="" } @Article{info:doi/10.5210/ojphi.v5i1.4484, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2013", volume="5", number="1", pages="e4484", doi="10.5210/ojphi.v5i1.4484", url="" } @Article{info:doi/10.5210/ojphi.v5i1.4492, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2013", volume="5", number="1", pages="e4492", doi="10.5210/ojphi.v5i1.4492", url="" } @Article{info:doi/10.5210/ojphi.v5i1.4515, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2013", volume="5", number="1", pages="e4515", doi="10.5210/ojphi.v5i1.4515", url="" } @Article{info:doi/10.5210/ojphi.v5i1.4533, title="Roles of Health Literacy in Relation to Social Determinants of Health and Recommendations for Informatics-Based Interventions: Systematic Review", journal="Online J Public Health Inform", year="2013", volume="5", number="1", pages="e4533", doi="10.5210/ojphi.v5i1.4533", url="" } @Article{info:doi/10.2196/ijmr.2357, author="Li, Junhua and Seale, Holly and Ray, Pradeep and Rawlinson, William and Lewis, Lundy and MacIntyre, Raina C.", title="Issues Regarding the Implementation of eHealth: Preparing for Future Influenza Pandemics", journal="Interact J Med Res", year="2012", month="Dec", day="06", volume="1", number="2", pages="e20", keywords="eHealth", keywords="influenza pandemic", keywords="preparedness assessment", keywords="case study", abstract="Background: eHealth is a tool that may be used to facilitate responses to influenza pandemics. Prior to implementation of eHealth in the hospital setting, assessment of the organizational preparedness is an important step in the planning process. Including this step may increase the chance of implementation success. Objective: To identify the preparedness issues in relation to implementation of eHealth for future influenza pandemics. Methods: One hospital was selected in Australia for this study. We conducted 12 individual interviews to gather a rich data set in relation to eHealth preparedness in the context of the 2009 influenza A (H1N1) pandemic at this major teaching hospital. These participants' views were analyzed according to five main themes: (1) challenges in present practices or circumstances for pandemic responses, which indicates a need for change, (2) healthcare providers' exposure to eHealth, (3) organizational technological capacity to support an IT innovation for medical practices, (4) resource preparedness, and (5) socio-cultural issues in association with eHealth implementation in response to a pandemic. Results: This article reports a subset of the issues identified during the case study. These issues include, for example, poor sharing of patient health records, poor protection of patient privacy, clinicians' concerns about IT reliability and dissatisfaction with the software in use, clinicians' concerns about IT's impact on professional autonomy versus having inefficient IT support, and inefficient communication across departments in the form of consultation. Conclusions: Based on discussions with the participants and interpretation of their responses, we assessed the hospital's preparedness status and also identified areas of deficiency. Accordingly, we suggest possible solutions for the areas in need of improvement to facilitate eHealth implementation's success. The study results will also provide policymakers at national, state and local levels with insights to refine relevant public health policies for the planning and management of pandemics from the eHealth perspective. ", doi="10.2196/ijmr.2357", url="http://www.i-jmr.org/2012/2/e20/", url="http://www.ncbi.nlm.nih.gov/pubmed/23611788" } @Article{info:doi/10.2196/jmir.1881, author="Sugawara, Tamie and Ohkusa, Yasushi and Ibuka, Yoko and Kawanohara, Hirokazu and Taniguchi, Kiyosu and Okabe, Nobuhiko", title="Real-time Prescription Surveillance and its Application to Monitoring Seasonal Influenza Activity in Japan", journal="J Med Internet Res", year="2012", month="Jan", day="16", volume="14", number="1", pages="e14", keywords="Surveillance", keywords="influenza", keywords="real-time surveillance", keywords="prescriptions", keywords="pharmacy", keywords="anti-influenza virus", keywords="automatic surveillance", keywords="early response", abstract="Background: Real-time surveillance is fundamental for effective control of disease outbreaks, but the official sentinel surveillance in Japan collects information related to disease activity only weekly and updates it with a 1-week time lag. Objective: To report on a prescription surveillance system using electronic records related to prescription drugs that was started in 2008 in Japan, and to evaluate the surveillance system for monitoring influenza activity during the 2009--2010 and 2010--2011 influenza seasons. Methods: We developed an automatic surveillance system using electronic records of prescription drug purchases collected from 5275 pharmacies through the application service provider's medical claims service. We then applied the system to monitoring influenza activity during the 2009--2010 and 2010--2011 influenza seasons. The surveillance system collected information related to drugs and patients directly and automatically from the electronic prescription record system, and estimated the number of influenza cases based on the number of prescriptions of anti-influenza virus medication. Then it shared the information related to influenza activity through the Internet with the public on a daily basis. Results: During the 2009--2010 influenza season, the number of influenza patients estimated by the prescription surveillance system between the 28th week of 2009 and the 12th week of 2010 was 9,234,289. In the 2010--2011 influenza season, the number of influenza patients between the 36th week of 2010 and the 12th week of 2011 was 7,153,437. The estimated number of influenza cases was highly correlated with that predicted by the official sentinel surveillance (r = .992, P < .001 for 2009--2010; r = .972, P < .001 for 2010--2011), indicating that the prescription surveillance system produced a good approximation of activity patterns. Conclusions: Our prescription surveillance system presents great potential for monitoring influenza activity and for providing early detection of infectious disease outbreaks. ", doi="10.2196/jmir.1881", url="http://www.jmir.org/2012/1/e14/", url="http://www.ncbi.nlm.nih.gov/pubmed/22249906" } @Article{info:doi/10.2196/jmir.1658, author="Cheng, KY Calvin and Ip, KM Dennis and Cowling, J. Benjamin and Ho, Ming Lai and Leung, M. Gabriel and Lau, HY Eric", title="Digital Dashboard Design Using Multiple Data Streams for Disease Surveillance With Influenza Surveillance as an Example", journal="J Med Internet Res", year="2011", month="Oct", day="14", volume="13", number="4", pages="e85", keywords="Dashboard", keywords="dissemination", keywords="surveillance", keywords="influenza", abstract="Background: Great strides have been made exploring and exploiting new and different sources of disease surveillance data and developing robust statistical methods for analyzing the collected data. However, there has been less research in the area of dissemination. Proper dissemination of surveillance data can facilitate the end user's taking of appropriate actions, thus maximizing the utility of effort taken from upstream of the surveillance-to-action loop. Objective: The aims of the study were to develop a generic framework for a digital dashboard incorporating features of efficient dashboard design and to demonstrate this framework by specific application to influenza surveillance in Hong Kong. Methods: Based on the merits of the national websites and principles of efficient dashboard design, we designed an automated influenza surveillance digital dashboard as a demonstration of efficient dissemination of surveillance data. We developed the system to synthesize and display multiple sources of influenza surveillance data streams in the dashboard. Different algorithms can be implemented in the dashboard for incorporating all surveillance data streams to describe the overall influenza activity. Results: We designed and implemented an influenza surveillance dashboard that utilized self-explanatory figures to display multiple surveillance data streams in panels. Indicators for individual data streams as well as for overall influenza activity were summarized in the main page, which can be read at a glance. Data retrieval function was also incorporated to allow data sharing in standard format. Conclusions: The influenza surveillance dashboard serves as a template to illustrate the efficient synthesization and dissemination of multiple-source surveillance data, which may also be applied to other diseases. Surveillance data from multiple sources can be disseminated efficiently using a dashboard design that facilitates the translation of surveillance information to public health actions. ", doi="10.2196/jmir.1658", url="http://www.jmir.org/2011/4/e85/", url="http://www.ncbi.nlm.nih.gov/pubmed/22001082" } @Article{info:doi/10.2196/jmir.1722, author="Hill, Shawndra and Mao, Jun and Ungar, Lyle and Hennessy, Sean and Leonard, E. Charles and Holmes, John", title="Natural Supplements for H1N1 Influenza: Retrospective Observational Infodemiology Study of Information and Search Activity on the Internet", journal="J Med Internet Res", year="2011", month="May", day="10", volume="13", number="2", pages="e36", keywords="Internet search", keywords="pandemic", keywords="herbal supplements", keywords="H1N1 influenza", abstract="Background: As the incidence of H1N1 increases, the lay public may turn to the Internet for information about natural supplements for prevention and treatment. Objective: Our objective was to identify and characterize websites that provide information about herbal and natural supplements with information about H1N1 and to examine trends in the public's behavior in searching for information about supplement use in preventing or treating H1N1. Methods: This was a retrospective observational infodemiology study of indexed websites and Internet search activity over the period January 1, 2009, through November 15, 2009. The setting is the Internet as indexed by Google with aggregated Internet user data. The main outcome measures were the frequency of ``hits'' or webpages containing terms relating to natural supplements co-occurring with H1N1/swine flu, terms relating to natural supplements co-occurring with H1N1/swine flu proportional to all terms relating to natural supplements, webpage rank, webpage entropy, and temporal trend in search activity. Results: A large number of websites support information about supplements and H1N1. The supplement with the highest proportion of H1N1/swine flu information was a homeopathic remedy known as Oscillococcinum that has no known side effects; supplements with the next highest proportions have known side effects and interactions. Webpages with both supplement and H1N1/swine flu information were less likely to be medically curated or authoritative. Search activity for supplements was temporally related to H1N1/swine flu-related news reports and events. Conclusions: The prevalence of nonauthoritative webpages with information about supplements in the context of H1N1/swine flu and the increasing number of searches for these pages suggest that the public is interested in alternatives to traditional prevention and treatment of H1N1. The quality of this information is often questionable and clinicians should be cognizant that patients may be at risk of adverse events associated with the use of supplements for H1N1. ", doi="10.2196/jmir.1722", url="http://www.jmir.org/2011/2/e36/", url="http://www.ncbi.nlm.nih.gov/pubmed/21558062" }