This paper is in the following e-collection/theme issue:

Reviews on Public Health Technology and Innovation (56) Public (e)Health, Digital Epidemiology and Public Health Informatics (636) Outbreak and Pandemic Preparedness and Management (1673) Infectious Diseases (non-STD/STI) (1478) Innovative Methods in Public Health and Surveillance (305) Prevention and Health Promotion (879)

Published on in Vol 10 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49185, first published .
Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review

Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review

Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review

Authors of this article:

Emily C Clark1 Author Orcid Image ;   Sophie Neumann1 Author Orcid Image ;   Stephanie Hopkins1 Author Orcid Image ;   Alyssa Kostopoulos1 Author Orcid Image ;   Leah Hagerman1 Author Orcid Image ;   Maureen Dobbins1, 2 Author Orcid Image
Article Authors Cited by Tweetations (5) Metrics

    Review

    1National Collaborating Centre for Methods and Tools, Hamilton, ON, Canada

    2School of Nursing, McMaster University, Hamilton, ON, Canada

    Corresponding Author:

    Maureen Dobbins, RN, PhD

    National Collaborating Centre for Methods and Tools

    175 Longwood Rd S

    Suite 210A

    Hamilton, ON, L8P 0A1

    Canada

    Phone: 1 9055259140 ext 20450

    Email: dobbinsm@mcmaster.ca


    Background: Public health surveillance plays a vital role in informing public health decision-making. The onset of the COVID-19 pandemic in early 2020 caused a widespread shift in public health priorities. Global efforts focused on COVID-19 monitoring and contact tracing. Existing public health programs were interrupted due to physical distancing measures and reallocation of resources. The onset of the COVID-19 pandemic intersected with advancements in technologies that have the potential to support public health surveillance efforts.

    Objective: This scoping review aims to explore emergent public health surveillance methods during the early COVID-19 pandemic to characterize the impact of the pandemic on surveillance methods.

    Methods: A scoping search was conducted in multiple databases and by scanning key government and public health organization websites from March 2020 to January 2022. Published papers and gray literature that described the application of new or revised approaches to public health surveillance were included. Papers that discussed the implications of novel public health surveillance approaches from ethical, legal, security, and equity perspectives were also included. The surveillance subject, method, location, and setting were extracted from each paper to identify trends in surveillance practices. Two public health epidemiologists were invited to provide their perspectives as peer reviewers.

    Results: Of the 14,238 unique papers, a total of 241 papers describing novel surveillance methods and changes to surveillance methods are included. Eighty papers were review papers and 161 were single studies. Overall, the literature heavily featured papers detailing surveillance of COVID-19 transmission (n=187). Surveillance of other infectious diseases was also described, including other pathogens (n=12). Other public health topics included vaccines (n=9), mental health (n=11), substance use (n=4), healthy nutrition (n=1), maternal and child health (n=3), antimicrobial resistance (n=2), and misinformation (n=6). The literature was dominated by applications of digital surveillance, for example, by using big data through mobility tracking and infodemiology (n=163). Wastewater surveillance was also heavily represented (n=48). Other papers described adaptations to programs or methods that existed prior to the COVID-19 pandemic (n=9). The scoping search also found 109 papers that discuss the ethical, legal, security, and equity implications of emerging surveillance methods. The peer reviewer public health epidemiologists noted that additional changes likely exist, beyond what has been reported and available for evidence syntheses.

    Conclusions: The COVID-19 pandemic accelerated advancements in surveillance and the adoption of new technologies, especially for digital and wastewater surveillance methods. Given the investments in these systems, further applications for public health surveillance are likely. The literature for surveillance methods was dominated by surveillance of infectious diseases, particularly COVID-19. A substantial amount of literature on the ethical, legal, security, and equity implications of these emerging surveillance methods also points to a need for cautious consideration of potential harm.

    JMIR Public Health Surveill 2024;10:e49185

    doi:10.2196/49185

    Keywords

    public health; surveillance; digital surveillance; COVID-19; screening; infodemiology; big data; mobility tracking; wastewater; ethics; decision making; public health surveillance 


    Decision-making in public health draws on many sources of evidence. The best available research evidence, local health issues and context, current community and political climate, and evidence for existing public health resources all contribute to an evidence-informed decision-making approach [1].

    Population-specific surveillance data provides critical evidence to inform decision makers on contextually relevant health issues. Public health surveillance data have been used to directly inform policies like animal importation laws; food recalls due to contaminated food; and communication to the public, such as promoting awareness of emerging environmental threats such as Lyme disease [2,3].

    Public health surveillance plays a vital role in decision-making for maintaining and promoting population health. While surveillance programs are most well-known for tracking infectious disease outbreaks, programs also include screening for noncommunicable diseases. In Canada, public health surveillance occurs at national, provincial or territorial, regional, and municipal levels. At the national level alone, nearly 30 surveillance programs, networks, and systems operate to monitor disease and illness across Canada—such as the National Enteric Surveillance Program which monitors the incidence of foodborne illness, and FluWatch, which reports on the spread of influenza [4,5]. There are also regional and local surveillance processes that collect data, including the Ontario integrated Public Health Information System, which allows for reporting and surveillance of several diseases including enteric illness and influenza [6]. The surveillance data collected through these programs inform public health planning, programming, and response.

    The COVID-19 pandemic and subsequent public health response led to a pivot in public health priorities. As the urgency to understand and respond to COVID-19 transmission grew, many public health teams prioritized pandemic response including the redeployment of many staff to contact tracing and eventually mass vaccination. For example, over 500 contact tracers were recruited in Ontario alone over a 2-month period in late 2020 [7], and, health care workers in British Columbia were hired or redistributed to form the COVID-19 Immunize BC Operations Centre to coordinate the province’s vaccine response [8]. Non–COVID-19 disease surveillance programs were impacted by emergency public health measures as well as reallocation of resources. Measures including physical distancing and service shutdowns impacted existing programs that were not considered essential, such as cancer screening programs that were paused or deferred during the height of the pandemic [9,10].

    Access to “smart” or networked technology has become ubiquitous both in and outside of public health, with nearly 90% of Canadians owning a smartphone and 75% of Canadians using social media regularly [11-13]. The progression of public health surveillance methods has reflected a similar integration of technology, seeing many changes since the prominent SARS-CoV-1 epidemic in 2003. Newer surveillance tools include electronic health records, advancements in wastewater surveillance infrastructure and technology, and digital health surveillance [14]. Electronic health records are the technological response to navigate the complex Canadian health care system by digitally housing individuals’ full health histories [15]. Digitization of records also allows for centrally tracking infectious diseases that may enter the country or region [14]. Prior to the COVID-19 pandemic, wastewater surveillance had gained popularity for tracking infectious disease dynamics and tracing the origins of foodborne illness outbreaks [16,17]. As it is difficult to estimate the true burden of disease of foodborne illnesses, wastewater surveillance has proven to be an accurate and relatively inexpensive way of collecting health data [17,18]. Similarly, digital surveillance can use internet search data to track where and when disease outbreaks occur [19]. More individually, connection to WiFi and GPS tracking can provide access to millions of smartphone users’ locations, enabling accurate and specific tracking of outbreaks and other public health issues [20,21].

    Despite advances that occurred prior to the COVID-19 pandemic, the need for improvement of public health surveillance capacity and methods has persisted in Canada and internationally [22-26]. Existing barriers in public health include low-quality data and obstacles to data sharing, as well as political and professional barriers such as incompatible technical lexicons and territorial borders [26]. This adds to the pertinence in investigating how the COVID-19 pandemic impacted public health surveillance and how it may continue to do so [27]. For example, the singular worldwide focus on COVID-19 allowed for an unprecedented dedication of resources (time, money, and intellect) to leverage pre-existing surveillance processes, creating the opportunity to apply these learnings to non–COVID-19 disease surveillance contexts [28]. The severity of the COVID-19 pandemic necessitated a further push in the development of innovative surveillance methods to track and manage SARS-CoV-2 transmission [29,30]. As a result, there have been marked improvements in the ability to predict, track, and respond to COVID-19 outbreaks in Canada and around the world [31-34].

    However, there is a need for ethical consideration to be at the forefront of these novel surveillance applications [18,35]. Although the ubiquity of personal technology like smartphones presents convenient and cost-effective disease surveillance opportunities, concerns around privacy and stigma through potential applications have emerged [36-39]. Digital surveillance tools that capture data through personal devices such as smartphones have the ability to capture personally identifiable information including race-based and health history data [35]. As digital surveillance itself is fairly novel there are few regulatory and legal frameworks in place to protect the misuse of this data [35]. Transparency, in terms of what information is being collected and how it is being used, is an additional ethical consideration that has yet to be fully addressed [38].

    The National Collaborating Centre for Methods and Tools’ Rapid Evidence Service [40] was established at the onset of the COVID-19 pandemic to conduct rapid reviews on priority public health topics. This review was requested of the rapid evidence service by the Public Health Agency of Canada to understand how public health surveillance was changed by the COVID-19 pandemic, resulting in a knowledge synthesis to inform future decision-making. This rapid scoping review investigates the question: What is known about changes to public health methods at the population level for governments globally due to the COVID-19 pandemic?


    Study Design

    A scoping review capturing literature on changes to public health surveillance in response to the COVID-19 pandemic was conducted and is reported according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Snalyses extension for Scoping Reviews) [41]. Review methods were based on previously described rapid review methods [40], with adaptations to a scoping review framework [42]. Inclusion and exclusion criteria were defined based on population, concept, and context criteria rather than population, intervention or exposure, comparison, and outcome criteria. Findings from included studies were not extracted and studies were not assessed for study quality, since the review question focused on identifying a breadth of evidence, rather than individual study findings. Data were analyzed for overall trends and presented visually.

    Information Sources and Search Strategy

    The search was conducted on January 25, 2022. The following databases were searched: Medline, Embase, Emcare, Global Health Database, PsycInfo, MedRxiv, COVID-19 living overview of evidence, Web of Science, and the World Health Organization’s global literature on coronavirus disease. Each data were searched using combinations of the terms “monitor,” “surveillance,” and “coronavirus.” Additionally, international government websites were hand-searched for relevant gray literature. The full search strategy is included in Multimedia Appendix 1 [14,28-30,32-34,36,43-274].

    Eligibility Criteria

    Literature on population-level surveillance was included if surveillance was established in response to the COVID-19 pandemic, or directly impacted by the COVID-19 pandemic, through reprioritization of resources or public health restrictions. Studies and reports published between March 1, 2020 and January 25, 2022, were included. While the first cases of COVID-19 were identified in December 2019, the search was timeframe limited to March 2020 or after to allow for literature to capture changes in surveillance and reduce the overall results set for this rapid scoping review. English- or French-language, peer-reviewed sources, and sources published ahead of print before peer review were included. Gray literature was also included. Detailed criteria for inclusion and rationale are outlined in Textbox 1.

    Textbox 1. Inclusion and exclusion criteria for rapid scoping review of changes to public health surveillance methods during the COVID-19 pandemic March 2020-January 2022.

    Population:

    • Inclusion criteriaNational, provincial, territorial, state, and regional population-level surveillance.
    • Exclusion criteriaSurveillance of an individual.
    • RationaleTo inform the planning of population-level surveillance programs and systems.

    Concept:

    • Inclusion criteriaSurveillance programs and systems that have been directly affected by the COVID-19 pandemic.
    • Exclusion criteriaSurveillance programs and systems not directly impacted by the COVID-19 pandemic.
    • RationaleTo explore shifts in surveillance approaches in response to the COVID-19 pandemic.

    Context:

    • Inclusion criteriaCOVID-19 pandemic: March 1, 2020, to January 25, 2022.
    • RationaleTo capture changes in surveillance methods due to the COVID-19 pandemic and reduce overall scope.

    Studies were screened using DistillerSR software (DistillerSR). Titles and abstracts of retrieved studies were screened by a single reviewer (SN). Full texts of included studies were screened by a second reviewer (ECC) and reviewed by a third (MD). The screening was not completed in duplicate to maintain a rapid timeline.

    Data Extraction

    Data extraction was completed by a single reviewer (ECC or SH). Details on the surveillance method (eg, wastewater sampling and digital data collection), as well as surveillance subjects (eg, COVID-19 and population mental health) for each included source were extracted. The type of evidence source was also extracted (eg, syntheses, single study, and opinion).

    Studies, critical reviews, editorials, and newspaper articles commenting on the ethics of surveillance methods that were captured by the search were briefly summarized. Details of the method of surveillance and the focus of the ethical implications (ie, ethics, security, legality, and equity) were extracted. Consistent with a scoping review approach, quality assessments of included studies were not completed.

    Data Analysis

    Sources were grouped by surveillance method and surveillance subject to identify overall trends in the literature. Papers presenting ethical implications of novel approaches to surveillance were grouped by the paper’s focus.

    Findings are presented as overall trends in the literature [42]. Results are discussed narratively and summarized in tables. To visually depict overall findings, a Sankey diagram was generated pairing surveillance methods and surveillance subjects, using the software SankeyMATIC (SankeyMATIC). Sankey diagrams can represent the flow of material or ideas through a system. The scale of each section of a Sankey diagram is proportional to the amount of material flowing between stages [43,275-277].

    Peer Reviewers

    Involving patient and public partners in rapid reviews has emerged as a valuable addition to review methodology. Gaining perspectives from patients and public partners can help ensure that rapid reviews meet knowledge users’ needs and are relevant for decision-making [278,279]. The Cochrane collaboration, a world leader in systematic review methodology, has identified consumer involvement throughout the process of evidence production and dissemination as a vital component [280,281]. Given the more technical focus of this review, rather than seeking partners with lived experience from the general public, epidemiologists who worked closely with surveillance efforts during the COVID-19 pandemic were invited as peer reviewers through organizational connections. Partners were invited to provide feedback on review findings based on lived experiences working in the field. Specifically, they were asked to review the Executive Summary and reflect on their own experiences with shifts in public health surveillance during the COVID-19 pandemic, and to highlight gaps in the literature. Their perspectives are not presented as a source of objective data alongside review findings, but rather as contextualizing perspectives of the objective data found by the review. Both partners provided written feedback that informed the review’s conclusions.


    Overview

    Database searching retrieved 36,791 records and scanning of key government websites retrieved 8 records. After removing duplicates, 14,238 records were screened by title and abstract, resulting in 1980 reports for full-text review. Of those 1980 records, 241 papers that described surveillance methods and 108 papers that discussed the implications of changes to public health surveillance programs from ethical, security, legal, and equity perspectives were included in the scoping review and underwent data extraction. See Figure 1 for a PRISMA-ScR flowchart illustrating the paper search and selection process.

    ‎
    Figure 1. PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) flow diagram of studies included in this scoping review of changes to public health surveillance during the COVID-19 pandemic from March 2020 through January 2022.

    Study Characteristics

    Of the 241 papers that described public health surveillance methods, 80 were review papers or evidence syntheses and 161 were single studies. Literature was dominated by reports of COVID-19 surveillance (n=187), but other infectious diseases were also represented, including other respiratory and viral pathogens (n=3), and gastrointestinal (n=3), sexually transmitted (n=2) and zoonotic infections (n=4). There were several papers reporting on surveillance related to immunization, including vaccine efficacy (n=1), vaccine safety monitoring (n=4), and vaccine hesitancy (n=4). Some papers reported on cancer screening (n=5) during the COVID-19 pandemic. Surveillance of mental health (n=11) and substance use (n=4) were also described. One paper described surveillance of healthy nutrition (n=1) and other papers described surveillance of maternal and child health (n=3). Papers described monitoring of antimicrobial resistance (n=2). Several papers described monitoring misinformation on the web (n=6). Table 1 summarizes the number of syntheses and single studies by public health surveillance subject, organized by public health topic area. A detailed list of included studies is available in Table S1 in Multimedia Appendix 1 [14,28-30,32-34,36,43-274].

    Table 1. Numbers of syntheses and single studies of public health surveillance method changes among public health surveillance subjects from March 2020 through January 2022.
    Public health topic area and surveillance subjectSyntheses, nSingle studies, n
    Antimicrobial stewardship

    		Antimicrobial resistance11
    Chronic diseases and conditions

    		Cancer screening14
    Health promotion

    		Nutrition10

    		Maternal and child health03
    Immunization

    		Vaccine efficacy01

    		Vaccine safety13

    		Vaccine hesitancy04
    Infectious diseases

    		COVID-1967120

    		Other respiratory pathogens (general)10

    		Other viral pathogens (general)11

    		Vaccine-preventable diseases01

    		Gastrointestinal infections12

    		Sexually transmitted infections11

    		Zoonoses13
    Mental health and substance use

    		Mental health29

    		Substance use13
    Health communication

    		Misinformation15

    Singles studies were conducted in the United States (n=51), Europe (n=27), East Asia (n=17), United Kingdom (n=9), Canada (n=5), South America (n=4), Africa (n=3), Australia (n=3), South Asia (n=2), and Russia (n=1). A total of 12 studies were conducted across several countries, and 27 studies were conducted globally. Global studies were mostly digital surveillance studies using global data sets.

    Of the 80 included evidence syntheses, the majority were nonsystematic literature reviews (n=61). Syntheses also included systematic reviews (n=10), scoping reviews (n=7), 1 rapid review, and 1 guideline.

    Surveillance Methods

    The Sankey diagram in Figure 2 illustrates the number of included papers that report on the various surveillance methods used for the target surveillance subjects, listed by public health topic area.

    ‎
    Figure 2. Sankey diagram representing the number of included papers for various surveillance methods used for the target surveillance subjects, listed by public health topic area, for the time period from March 2020 to January 2022. Please note that the numbers for each surveillance method may not reflect the total number of resources in this report, as some syntheses discuss more than 1 surveillance method.

    Most of the papers on surveillance of COVID-19 transmission described digital surveillance (eg, 130 papers on the use of big data through mobility tracking and infodemiology and 40 papers on wastewater epidemiology). Other methods for monitoring COVID-19 transmission were designed to supplement limited COVID-19 testing capacity, including analysis of data from various health care settings, for example, hospitals and blood banks (n=11). Strategies to track COVID-19 transmission and detect new SARS-CoV-2 variants through genome sequencing were also described (n=5).

    Literature for select public health surveillance functions that operated prior to the COVID-19 pandemic, such as cancer screening, child health assessments, and sexually transmitted infection screening, indicated that these functions adapted to using technology for remote screening in response to mobility restrictions and physical distancing. For example, 2 studies described videoconferencing for remote autism screening in young children, and another study described a pilot program for remote sexually transmitted infection self-testing under the supervision of public health staff through videoconference.

    Other public health surveillance programs took approaches similar to COVID-19 surveillance. For example, digital surveillance technologies were applied to monitor population mental health (n=8), misinformation (n=5), and zoonoses (n=2). In addition to COVID-19, wastewater surveillance was used to monitor antibiotic resistance (n=2), mental health (n=2), substance use (n=1), as well as other infectious diseases.

    Artificial intelligence (AI) was used to augment digital surveillance of COVID-19 transmission (n=29), immunization (n=2), substance use (n=2), and misinformation (n=3; Table 2). Most of these used AI to comb through population-level cell phone mobility data, social media content, or search engine queries for indications of COVID-19 transmission. AI was also used to analyze social media content and search engine queries for indications of vaccine hesitancy, substance use, and misinformation. One paper described using AI to analyze global wastewater data for COVID-19 transmission pathways.

    Table 2. Summary of the number of papers for each new method of public health surveillance from March 2020 through January 2022.
    MethodNumber of included papersNumber using artificial intelligence
    Adaptation to existing practice90
    AI-driven modeling33
    Digital surveillance16336

    		Data standardization40

    		Participatory394

    		Big data13132


    		Mobility tracking3612


    		Proximity monitoring50


    		Infodemiology8622
    Health care setting110
    Indigenous methods10
    Wastewater481

    Several studies (n=4) described the development of standardized, digitized data repositories to enable interjurisdictional surveillance [44-47]. One of these described standardized electronic reporting for cancer screening to support central cancer registries [44]. A literature review described distributed data networks to monitor COVID-19 vaccine safety issues [45]. Two papers described data federation technologies for infectious diseases other than COVID-19. A literature review by Baker et al [46] described the standardization of data for malaria, and a pilot study by Colella et al [47] described the development of a biorepository platform to monitor animal-to-human disease transmission. These papers emphasized the interoperability of data systems to facilitate large-scale surveillance of public health issues.

    One study used Indigenous methods to monitor COVID-19 through a community-focused COVID-19 surveillance program [48]. In this study, the Hopi Tribe, a sovereign nation in the United States, collaborated with the US Centers for Disease Control and Prevention to deploy community health representatives to every household in 2 Hopi villages [48]. Community health representatives screened household members for COVID-19 symptoms, exposures, and provided education, all in a culturally safe manner [48]. No other studies using Indigenous methods were found.

    Ethical Implications

    In addition to papers describing public health surveillance methods, 108 papers exploring the implications of changes to public health surveillance programs during the COVID-19 pandemic, from ethical, legal, data security, and equity perspectives were retrieved. These papers focused on digital surveillance (n=104) or wastewater surveillance (n=4) and discussed potential consequences of surveillance in terms of ethics (n=80), data security (n=28), legality (n=28), and equity (n=21), noting that some papers covered more than 1 topic area. A detailed list of included studies is available in Table S2 in Multimedia Appendix 2 [37-39,282-386].

    Aside from 2 systematic reviews, most of the papers discussing the implications of surveillance were critical literature reviews, conducted nonsystematically and written to express a clear opinion or bias (n=71). There were 36 editorials that did not cite or infrequently cited other literature and were published in academic journals (n=29) or newspapers (n=7).

    Perspectives From Public Health Epidemiologists

    Two public health epidemiologists who worked in Ontario during the COVID-19 pandemic provided feedback on the findings of this review. They suggested that nearly every surveillance system or program was impacted by the COVID-19 pandemic; some paused due to resource reallocation. Several key metrics that were previously reliable indicators became difficult or not possible to interpret due to the impact of COVID-19. These changes may not be captured in academic literature featured in this review, as many public health professionals working during the pandemic simply did not have the time, energy, or academic support to publish their work.

    Epidemiologists shared that the overall approach to surveillance of COVID-19 also changed over the course of the pandemic. While case counts were an early key indicator, there has been a general shift to measures such as hospital and intensive care unit occupancy, reflecting the change in priority from eliminating COVID-19 altogether to reducing and managing the health care system burden.

    It was also suggested by epidemiologists that while data for equity has been limited, some governments have made efforts to improve measures of equity. For example, in 2020 the Ontario government amended the Health Protection and Promotion Act and augmented the provincial case and contact management tool to allow for the collection of social determinants (eg, race and income) for COVID-19 cases. Similar changes were made for COVID-19 vaccine surveillance data.

    It was suggested that substantial resources are required to restore public health surveillance systems during pandemic recovery and improvements can be made by leveraging or investing in better data.


    Principal Findings

    This scoping review provides a comprehensive overview of changes to public health surveillance methods for programs, systems, and strategies at the population level implemented since early 2020. The review includes 241 papers, including 80 evidence syntheses and 161 single studies describing surveillance methods, as well as 108 papers discussing the implications of changes to public health surveillance programs from ethical, security, legal, and equity perspectives. A Sankey diagram (Figure 2) illustrates the dominance of COVID-19 surveillance in this literature, particularly through digital surveillance strategies.

    The heavy focus on COVID-19 in the surveillance literature reflects public health’s drastic reallocation of resources to the pandemic response [387,388]. As nearly all countries of the world entered some form of public health lockdown, unique research opportunities presented themselves [389,390]. Health researchers redirected efforts to contribute to global focus to understand and mitigate COVID-19 transmission and harm [391]. Additionally, academic journals expedited the publication process for papers on COVID-19 [392,393]. While other public health subjects, such as maternal and child health, chronic diseases, and infectious diseases other than COVID-19 were also represented in the surveillance literature, these subjects were largely overshadowed by COVID-19. As suggested by the public health epidemiologists included in this review, those working in surveillance for other public health topics likely did not have time or resources to publish during the pandemic.

    Within the COVID-19 surveillance literature, a substantial proportion of papers discussed digital forms of surveillance. This was largely using sources of big data, such as social media posts, search engine queries, and mobility tracking. Prior to the COVID-19 pandemic, there had been a steady rise in publications on big data, as such the field was sufficiently established to contribute to COVID-19 tracking and analysis at the onset of the pandemic [394,395]. Publications were largely proof-of-concept, demonstrating if and how big data analysis correlated with levels of COVID-19 infections seen in communities. Following the interest and advancements in the analysis of big data, these methods are likely to continue to see novel applications in public health.

    Wastewater surveillance emerged as a dominant method in the COVID-19 surveillance literature. Wastewater testing is an attractive option for disease surveillance as it is a cost-effective method to monitor populations without requiring active participation or invasive testing procedures [396-398]. Similar to the literature for big data analysis, much of the literature on wastewater provided proof of concept by correlating wastewater detection of the SARS-CoV-2 virus with reported levels of COVID-19 in the community. The impact of wastewater surveillance on public health decision-making is likely to grow [399]. Recently, the detection of vaccine-derived poliovirus in wastewater in New York City led to the addition of the United States to the World Health Organization’s list of countries with polio transmission [400]. To expand coverage across Canada’s population, the Public Health Agency of Canada invested in a pan-Canadian wastewater surveillance network for COVID-19, in collaboration with other federal departments; provincial, territorial, and municipal governments; and academic institutions [401]. While the focus for wastewater surveillance was largely on COVID-19, advances in wastewater monitoring capacity allowed for progress in wastewater monitoring of other conditions and issues, such as mental health and substance use. Given the investment in wastewater epidemiology and advancements in its applications, it is likely to emerge as a key indicator for public health post the pandemic.

    In the context of COVID-19 pandemic lockdowns and physical distancing, it is not surprising that some public health services, such as childhood nutrition, cancer screening, and sexual health, were adapted to remote provision of services. While several papers describing the implementation of remote services were included in this review, it is likely that these papers represent only a small sample of public health efforts to continue service provision during the pandemic. As indicated by the public health epidemiologists consulted for this review, many changes to existing public health surveillance programs are likely not published in the academic literature, due to the demands on public health professionals’ time.

    The COVID-19 pandemic also highlighted the importance of interjurisdictional data systems which can enable timely access to data across jurisdictional boundaries to inform public health functions. Several papers included in this review explored standardization of data to enhance the public health response, for monitoring zoonoses [46,47] or rare vaccine-related harms [45] at a population level. Solutions to the current limitations of data interoperability must be implemented in future public health surveillance efforts to improve access to essential data in real-time that can inform decision-making within critical health organizations and governing bodies.

    While technology has enabled many advancements in public health surveillance, the findings of this scoping review also demonstrate the alarm caused by these changes. The ethical implications of digital surveillance, in particular, were discussed in 80 included papers. Since smartphones and the internet of things (a system of interrelated computing devices to connect people or things identified by unique identifiers), have only become widely used relatively recently, with the potential consequences of their use in public health surveillance not being fully realized yet. The COVID-19 pandemic accelerated the development and testing of digital surveillance, and the literature reflects that experts in these fields have cause for concern. The relative lack of literature discussing the impact of changes in public health surveillance on equity is also concerning. The pandemic exacerbated existing inequities, and care must be taken so that these emerging methods do not further widen equity gaps [402-406]. Data collection and analysis for equity-deserving populations must be a priority as the identification of inequities allows for mitigation. However, these data must be collected and handled with care and respect to avoid potential harm and stigmatization [407,408].

    Limitations

    Two public health epidemiologists who contributed their perspectives to this review raised that many changes to surveillance are not captured in the literature, primarily due to the lack of time and academic support for public health in publishing. This is an important limitation of this review, as the full scope of changes to public health surveillance is likely not represented. Academic partnerships and support for public health can help share innovations [409].

    The peer perspectives shared in this review are also limited to 2 epidemiologists based in Canada, which may not fully represent the perspectives of those involved in public health surveillance worldwide. However, the epidemiologists involved provided contextual insights to the findings of this review.

    This review is also limited to papers from March 2020 to January 2022. Studies published after January 2022 may be more likely to capture changes to public health surveillance that are not directly related to COVID-19. For example, examples of wastewater surveillance of pathogens other than SARS-CoV-2 may not have been captured by this search.

    As a scoping review of the literature for changes to public health surveillance methods during the COVID-19 pandemic, there are limitations to the overall conclusions that can be supported by this review’s findings. Given the scoping review approach, findings for the effectiveness of surveillance methods and the quality of included papers were not evaluated. A systematic analysis of included papers, including the extraction of outcome data, rigorous quality assessment of included papers, and judgments of the certainty of evidence, is strongly recommended when considering the potential future implementation of any of the surveillance methods discussed in this report.

    Conclusions

    A considerable body of literature emerged related to advances in surveillance during the COVID-19 pandemic. Some areas have seen much more literature, such as wastewater epidemiology, while others remain sparse, such as health equity for racialized populations. Possible next steps include conducting systematic reviews in areas for which multiple studies have been identified. In other areas with sparse literature, more research is needed to advance surveillance in the postpandemic era. A large volume of papers expressing concerns about the potential ethical, legal, security, and equity implications of emerging surveillance methods indicates a need for a thorough assessment of the potential effects of large-scale implementation of these surveillance methods.

    Acknowledgments

    The authors would like to acknowledge epidemiologists Jasmine Song and Mackenzie Slifierz for sharing their perspectives on public health surveillance as peer partners in this review. The National Collaborating Centre for Methods and Tools is hosted by McMaster University and funded by the Public Health Agency of Canada. The views expressed herein do not necessarily represent the views of the Public Health Agency of Canada. The funder had no role in the design of the study, collection, analysis, or interpretation of data or in writing the manuscript.

    Data Availability

    The data sets used and analyzed in the conduct of this review are available from the corresponding authors on reasonable request.

    Authors' Contributions

    ECC, LH, and MD designed the study. SN, SH, AK, and ECC completed screening, and data extraction. ECC, SH, and MD analyzed the study results. AK prepared the Sankey diagram. ECC, SN, and LH wrote the manuscript in consultation with MD. SN, SH, AK, LH, and MD provided feedback on all drafts and the final version of the manuscript.

    Conflicts of Interest

    None declared.

    Multimedia Appendix 1

    Included articles that describe public health surveillance methods.

    DOCX File , 251 KB
    Multimedia Appendix 2

    Included articles that describe implications of emerging public health surveillance methods.

    DOCX File , 72 KB
    Multimedia Appendix 3

    PRISMA-ScR Checklist.

    PDF File (Adobe PDF File), 243 KB
    1. Evidence-informed decision making in public health. National Collaborating Centre for Methods and Tools. 2022. URL: https://www.nccmt.ca/tools/eiph [accessed 2023-12-23]
    2. Importing terrestrial animals to Canada. Canadian Food Inspection Agency. 2022. URL: http://tinyurl.com/4yzs8bec [accessed 2023-12-23]
    3. Lyme disease. Centers for Disease Control and Prevention. 2022. URL: https://www.cdc.gov/lyme/toolkit/index.html [accessed 2023-12-23]
    4. Public health surveillance. Public Health Agency of Canada. 2022. URL: http://tinyurl.com/mupdzuxj [accessed 2023-12-23]
    5. National Enteric Surveillance Program (NESP). Public Health Agency of Canada. 2022. URL: http://tinyurl.com/539mf5rr [accessed 2023-12-23]
    6. iPHIS resources. Ontario PH. 2022. URL: http://tinyurl.com/yv3et4pz [accessed 2023-12-23]
    7. Ontario hiring hundreds more contact tracers and case managers 2020. Premier Oot. 2022. URL: http://tinyurl.com/4288cybm [accessed 2023-12-23]
    8. COVID-19 immunize BC operations centre: standard operating procedures. BC Centre for Disease Control. 2022. URL: http://tinyurl.com/yrb2hu75 [accessed 2023-12-23]
    9. COVID-19 intervention timeline in Canada. Canadian Institute for Health Information. 2022. URL: https://www.cihi.ca/en/covid-19-intervention-timeline-in-canada [accessed 2023-12-23]
    10. Cancer screening during COVID-19: message for the public. Cancer Care Ontario. 2022. URL: https://www.cancercareontario.ca/en/covid19-cancer/screening-during-covid-19 [accessed 2023-12-23]
    11. Archived—smartphone use and smartphone habits by gender and age group, inactive. Statistics Canada. 2021. URL: https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=2210011501 [accessed 2023-12-23]
    12. The evolution of social media: how did it begin, and where could it go next? Maryville University. 2022. URL: https://online.maryville.edu/blog/evolution-social-media/ [accessed 2023-12-23]
    13. Bilodeau H, Kehler A, Minnema N. Internet use and COVID-19: how the pandemic increased the amount of time Canadians spend online. Canadians Spend. 2021. URL: http://tinyurl.com/4z9xvz56 [accessed 2023-12-23]
    14. Peterson KS, Lewis J, Patterson OV, Chapman AB, Denhalter DW, Lye PA, et al. Automated travel history extraction from clinical notes for informing the detection of emergent infectious disease events: algorithm development and validation. JMIR Public Health Surveill. 2021;7(3):e26719. [FREE Full text] [CrossRef] [Medline]
    15. What's an EHR? eHealth Ontario. 2022. URL: https://ehealthontario.on.ca/en/patients-and-families/ehrs-explained [accessed 2023-12-23]
    16. Brinkman NE, Fout GS, Keely SP. Retrospective surveillance of wastewater to examine seasonal dynamics of enterovirus infections. mSphere. 2017;2(3):e00099-e10017. [FREE Full text] [CrossRef] [Medline]
    17. Zhou L, Singh A, Jiang J, Xiao L. Molecular surveillance of cryptosporidium spp. in raw wastewater in Milwaukee: implications for understanding outbreak occurrence and transmission dynamics. J Clin Microbiol. 2003;41(11):5254-5257. [FREE Full text] [CrossRef] [Medline]
    18. Arefin MR, Prouse C, Reimer C. Targeted wastewater surveillance has a history of social and ethical concerns. The Conversation. 2022. URL: http://tinyurl.com/mren6ubh [accessed 2023-12-23]
    19. Brownstein JS, Freifeld CC, Madoff LC. Digital disease detection--harnessing the web for public health surveillance. N Engl J Med. 2009;360(21):2153-2155. [FREE Full text] [CrossRef] [Medline]
    20. Operator-based location data and services for public health mobility analysis (1000236419). Public Services and Procurement Canada. 2022. URL: http://tinyurl.com/y8bs22cd [accessed 2023-12-23]
    21. Rockwell GM, Berendt B, Chee FM, Matthews J, Gambs S. Ottawa's use of our location data raises big surveillance and privacy concerns. The Conversation. 2022. URL: http://tinyurl.com/2m2vbzh4 [accessed 2023-12-23]
    22. Choi BCK. The past, present, and future of public health surveillance. Scientifica (Cairo). 2012;2012:875253. [FREE Full text] [CrossRef] [Medline]
    23. Reeder B, Revere D, Hills RA, Baseman JG, Lober WB. Public health practice within a health information exchange: information needs and barriers to disease surveillance. Online J Public Health Inform. 2012;4(3):ojphi.v4i3.4277. [FREE Full text] [CrossRef] [Medline]
    24. van Panhuis WG, Paul P, Emerson C, Grefenstette J, Wilder R, Herbst AJ, et al. A systematic review of barriers to data sharing in public health. BMC Public Health. 2014;14(1):1144. [FREE Full text] [CrossRef] [Medline]
    25. Uchtmann N, Herrmann JA, Hahn EC, Beasley VR. Barriers to, efforts in, and optimization of integrated one health surveillance: a review and synthesis. Ecohealth. 2015;12(2):368-384. [FREE Full text] [CrossRef] [Medline]
    26. Global public health intelligence network (GPHIN) independent review final report. Canada PHAo. 2021. URL: http://tinyurl.com/4w3t88ne [accessed 2023-12-23]
    27. Najah R. Les technologies de surveillance à l'ère de la Covid-19 / surveillance technologies in the COVID-19 Era. Policy Commons. 2020 [FREE Full text]
    28. Rana A, Malik S. Leveraging emerging technology to combat COVID-19 pandemic emphasize on AI. Int J Eme Technol Innov Res. 2020;7(12):133-145. [FREE Full text]
    29. Quinn E, Hsiao KH, Maitland-Scott I, Gomez M, Baysari MT, Najjar Z, et al. Web-based apps for responding to acute infectious disease outbreaks in the community: systematic review. JMIR Public Health Surveill. 2021;7(4):e24330. [FREE Full text] [CrossRef] [Medline]
    30. Kilaru P, Hill D, Anderson K, Collins MB, Green H, Kmush BL, et al. Wastewater surveillance for infectious disease: a systematic review. Am J Epidemiol. 2023;192(2):305-322. [FREE Full text] [CrossRef] [Medline]
    31. Safranek CW, Scheinker D. A computer modeling method to analyze rideshare data for the surveillance of novel strains of SARS-CoV-2. Ann Epidemiol. 2022;76:136-142. [FREE Full text] [CrossRef] [Medline]
    32. Brakefield WS, Ammar N, Olusanya O, Ozdenerol E, Thomas F, Stewart AJ, et al. Implementing an urban public health observatory for (Near) real-time surveillance for the COVID-19 pandemic. Stud Health Technol Inform. 2020;275:22-26. [CrossRef] [Medline]
    33. Alamo T, Reina DG, Gata PM, Preciado VM, Giordano G. Data-driven methods for present and future pandemics: monitoring, modelling and managing. Annu Rev Control. 2021;52:448-464. [FREE Full text] [CrossRef] [Medline]
    34. Ouyang L, Yuan Y, Cao Y, Wang F. A novel framework of collaborative early warning for COVID-19 based on blockchain and smart contracts. Inf Sci (N Y). 2021;570:124-143. [FREE Full text] [CrossRef] [Medline]
    35. Sundquist CP. Pandemic surveillance discrimination. Seton Hall Law Rev. 2021;1535:51. [FREE Full text]
    36. Jalabneh R, Syed HZ, Pillai S, Apu EH, Hussein MR, Kabir R, et al. Use of mobile phone apps for contact tracing to control the COVID-19 pandemic: a literature review. SSRN J. 2020 [CrossRef]
    37. Afroogh S, Esmalian A, Mostafavi A, Akbari A, Rasoulkhani K, Esmaeili S, et al. Tracing app technology: an ethical review in the COVID-19 era and directions for post-COVID-19. Ethics Inf Technol. 2022;24(3):30. [FREE Full text] [CrossRef]
    38. Bruneau GA, Gilthorpe M, Muller VC. The ethical imperatives of the COVID-19 pandemic: a review from data ethics. Veritas. 2020;46:13-35. [FREE Full text]
    39. Sundquist C. Pandemic surveillance discrimination. SSRN. 2021;51:1535-1547. [FREE Full text]
    40. Neil-Sztramko SE, Belita E, Traynor RL, Clark E, Hagerman L, Dobbins M. Methods to support evidence-informed decision-making in the midst of COVID-19: creation and evolution of a rapid review service from the National Collaborating Centre for methods and tools. BMC Med Res Methodol. 2021;21(1):231. [FREE Full text] [CrossRef] [Medline]
    41. Tricco AC, Lillie E, Zarin W, O'Brien KK, Colquhoun H, Levac D, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169(7):467-473. [FREE Full text] [CrossRef] [Medline]
    42. Arksey H, O'Malley L. Scoping studies: towards a methodological framework. Int J Soc Res Methodol. 2005;8(1):19-32. [FREE Full text] [CrossRef]
    43. Gong M, Liu L, Sun X, Yang Y, Wang S, Zhu H. Cloud-based system for effective surveillance and control of COVID-19: useful experiences from Hubei, China. J Med Internet Res. 2020;22(4):e18948. [FREE Full text] [CrossRef] [Medline]
    44. Jones DE, Alimi TO, Pordell P, Tangka FK, Blumenthal W, Jones SF, et al. Pursuing data modernization in cancer surveillance by developing a cloud-based computing platform: real-time cancer case collection. JCO Clin Cancer Inform. 2021;5:24-29. [FREE Full text] [CrossRef] [Medline]
    45. Dodd C, Andrews N, Petousis-Harris H, Sturkenboom M, Omer SB, Black S. Methodological frontiers in vaccine safety: qualifying available evidence for rare events, use of distributed data networks to monitor vaccine safety issues, and monitoring the safety of pregnancy interventions. BMJ Glob Health. 2021;6(Suppl 2):e003540. [FREE Full text] [CrossRef] [Medline]
    46. Baker CJO, Manir MSA, Brenas JH, Zinszer K, Shaban-Nejad A. Applied ontologies for global health surveillance and pandemic intelligence. medRxiv. Preprint posted online on October 20, 2020. [FREE Full text] [CrossRef]
    47. Colella JP, Bates J, Burneo SF, Camacho MA, Bonilla CC, Constable I, et al. Leveraging natural history biorepositories as a global, decentralized, pathogen surveillance network. PLoS Pathog. 2021;17(6):e1009583. [FREE Full text] [CrossRef] [Medline]
    48. Jenkins R, Burke RM, Hamilton J, Fazekas K, Humeyestewa D, Kaur H, et al. Notes from the field: development of an enhanced community-focused COVID-19 surveillance program—hopi tribe, June-July 2020. MMWR Morb Mortal Wkly Rep. 2020;69(44):1660-1661. [FREE Full text] [CrossRef] [Medline]
    49. Pruden A, Vikesland PJ, Davis BC, de Roda Husman AM. Seizing the moment: now is the time for integrated global surveillance of antimicrobial resistance in wastewater environments. Curr Opin Microbiol. 2021;64:91-99. [CrossRef] [Medline]
    50. Riquelme MVR, Garner E, Gupta S, Metch J, Zhu N, Blair MF, et al. Demonstrating a comprehensive wastewater-based surveillance approach that differentiates globally sourced resistomes. Environ Sci Technol. 2022;56(21):14982-14993. [FREE Full text] [CrossRef] [Medline]
    51. Mehta N, Parikh ND, Kelley RK, Hameed B, Singal AG. Surveillance and monitoring of hepatocellular carcinoma during the COVID-19 pandemic. Clin Gastroenterol Hepatol. 2021;19(8):1520-1530. [FREE Full text] [CrossRef] [Medline]
    52. Maresca G, Akinlua A, Wardlaw K, Macdonald G, Royle J. Coronavirus disease 2019 and the cancellation of face-to-face prostate cancer active surveillance clinic reviews: does the omission of digital rectal examination alone put patients at risk of missed detection of disease progression? J Clin Urol. 2021;15(2):103-108. [FREE Full text] [CrossRef]
    53. Sinharay R, Gelson W. Prioritising cancer surveillance in chronic hepatitis B in the COVID-19 era. time to stop and think? Hepatology. 2020:405A.
    54. Xiao AH, Chang SY, Stevoff CG, Komanduri S, Pandolfino JE, Keswani RN. Adoption of multi-society guidelines facilitates value-based reduction in screening and surveillance colonoscopy volume during COVID-19 pandemic. Dig Dis Sci. 2021;66(8):2578-2584. [FREE Full text] [CrossRef] [Medline]
    55. Nutrition information management, surveillance and monitoring in the context of COVID-19. UNICEF GNC, Global Technical Assistance Mechanism for Nutrition (GTAM). 2020. URL: https://www.ennonline.net/covid19nisbrief [accessed 2023-12-23]
    56. Eapen V, Woolfenden S, Schmied V, Jalaludin B, Lawson K, Liaw ST, et al. "Watch Me Grow-Electronic (WMG-E)" surveillance approach to identify and address child development, parental mental health, and psychosocial needs: study protocol. BMC Health Serv Res. 2021;21(1):1240. [FREE Full text] [CrossRef] [Medline]
    57. Maitre NL, Benninger KL, Neel ML, Haase JA, Pietruszewski L, Levengood K, et al. Standardized neurodevelopmental surveillance of high-risk infants using telehealth: implementation study during COVID-19. Pediatr Qual Saf. 2021;6(4):e439. [FREE Full text] [CrossRef] [Medline]
    58. Conti E, Chericoni N, Costanzo V, Lasala R, Mancini A, Prosperi M, et al. Moving toward telehealth surveillance services for toddlers at risk for autism during the COVID-19 pandemic. Front Psychiatry. 2020;11:565999. [FREE Full text] [CrossRef] [Medline]
    59. Varrelman TJ, Rader B, Astley CM, Brownstein JS. Syndromic surveillance-based estimates of vaccine efficacy against COVID-like illness from emerging Omicron and COVID-19 variants. medRxiv. Preprint posted online on December 18, 2021. [FREE Full text] [CrossRef]
    60. Guedel DS, Peters IJ, Banderet F, Epple V, Egli S, Mehling M, et al. Smartphone-based active vaccine safety surveillance (SmartVax) at a Swiss adult vaccination clinic—a pilot study. Swiss Med Wkly. 2021;151:w30090. [FREE Full text] [CrossRef] [Medline]
    61. Salter SM, Singh G, Nissen L, Trentino K, Murray K, Lee K, et al. Active vaccine safety surveillance of seasonal influenza vaccination via a scalable, integrated system in Western Australian pharmacies: a prospective cohort study. BMJ Open. 2021;11(6):e048109. [FREE Full text] [CrossRef] [Medline]
    62. Schuemie MJ, Arshad F, Pratt N, Nyberg F, Alshammari TM, Hripcsak G, et al. Vaccine safety surveillance using routinely collected healthcare data-an empirical evaluation of epidemiological designs. Front Pharmacol. 2022;13:893484. [FREE Full text] [CrossRef] [Medline]
    63. Beeferman D, Au L, Abdul-Khalek RA, Desai A, Majumder MS. An inductive approach to using Google search trends to identify attitudes and areas of COVID-19 vaccine hesitancy. SSRN J. 2021 [CrossRef]
    64. Liew TM, Lee CS. Examining the utility of social media in COVID-19 vaccination: unsupervised learning of 672,133 Twitter posts. JMIR Public Health Surveill. 2021;7(11):e29789. [FREE Full text] [CrossRef] [Medline]
    65. Zhou X, de Figueiredo A, Xu Q, Lin L, Kummervold PE, Larson H, et al. Monitoring global trends in Covid-19 vaccination intention and confidence: a social media-based deep learning study. medRxiv. Preprint posted online on April 25, 2021. [FREE Full text] [CrossRef]
    66. Rovetta A. Google trends as a predictive tool for COVID-19 vaccinations in Italy: a retrospective infodemiological analysis. JMIRx Med. 2022;3(2):e35356. [FREE Full text] [CrossRef]
    67. Carr VR, Chaguza C. Metagenomics for surveillance of respiratory pathogens. Nat Rev Microbiol. 2021;19(5):285. [FREE Full text] [CrossRef] [Medline]
    68. Mohsin H, Asif A, Fatima M, Rehman Y. Potential role of viral metagenomics as a surveillance tool for the early detection of emerging novel pathogens. Arch Microbiol. 2021;203(3):865-872. [FREE Full text] [CrossRef] [Medline]
    69. Nieuwenhuijse DF, Munnink BBO, Phan MVT, Munk P, Venkatakrishnan S, Aarestrup FM, et al. Setting a baseline for global urban virome surveillance in sewage. Sci Rep. 2020;10(1):13748. [FREE Full text] [CrossRef] [Medline]
    70. Obeidat R, Alsmadi I, Bakr QB, Obeidat L. Can users search trends predict people scares or disease breakout? an examination of infectious skin diseases in the United States. Infect Dis (Auckl). 2020;13:1178633720928356. [FREE Full text] [CrossRef] [Medline]
    71. Yuan K, Huang G, Wang L, Wang T, Liu W, Jiang H, et al. Predicting Norovirus in the United States using Google trends: infodemiology study. J Med Internet Res. 2021;23(9):e24554. [FREE Full text] [CrossRef] [Medline]
    72. Zahedi A, Monis P, Deere D, Ryan U. Wastewater-based epidemiology-surveillance and early detection of waterborne pathogens with a focus on SARS-CoV-2, Cryptosporidium and Giardia. Parasitol Res. 2021;120(12):4167-4188. [FREE Full text] [CrossRef] [Medline]
    73. Tedcastle A, Wilton T, Pegg E, Klapsa D, Bujaki E, Mate R, et al. Detection of enterovirus D68 in wastewater samples from the UK between July and November 2021. Viruses. 2022;14(1):143. [FREE Full text] [CrossRef] [Medline]
    74. Rivera A, Martinez P, Braunstein S. Video-based HIV testing services among cisgender men who have sex with men as a component of an HIV behavioural surveillance study during the COVID-19 pandemic in New York City: implementation, feasibility and lessons learned. Sex Transm Infect. 2022;98(4):310-311. [CrossRef] [Medline]
    75. Kersh EN, Shukla M, Raphael BH, Habel M, Park I. At-home specimen self-collection and self-testing for sexually transmitted infection screening demand accelerated by the COVID-19 pandemic: a review of laboratory implementation issues. J Clin Microbiol. 2021;59(11):e0264620. [FREE Full text] [CrossRef] [Medline]
    76. Tizzani M, Muñoz-Gómez V, De Nardi M, Paolotti D, Muñoz O, Ceschi P, et al. Integrating digital and field surveillance as complementary efforts to manage epidemic diseases of livestock: African swine fever as a case study. PLoS One. 2021;16(12):e0252972. [FREE Full text] [CrossRef] [Medline]
    77. Brogly C, Bauer MA, Lizotte DJ, Press ML, MacDougall A, Speechley M, et al. An app-based surveillance system for undergraduate students' mental health during the COVID-19 pandemic: protocol for a prospective cohort study. JMIR Res Protoc. 2021;10(9):e30504. [FREE Full text] [CrossRef] [Medline]
    78. Cohrdes C, Yenikent S, Wu J, Ghanem B, Franco-Salvador M, Vogelgesang F. Indications of depressive symptoms during the COVID-19 pandemic in Germany: comparison of national survey and Twitter data. JMIR Ment Health. 2021;8(6):e27140. [FREE Full text] [CrossRef] [Medline]
    79. Gimbrone C, Rutherford C, Kandula S, Martínez-Alés G, Shaman J, Olfson M, et al. Associations between COVID-19 mobility restrictions and economic, mental health, and suicide-related concerns in the US using cellular phone GPS and Google search volume data. PLoS One. 2021;16(12):e0260931. [FREE Full text] [CrossRef] [Medline]
    80. Guntuku SC, Sherman G, Stokes DC, Agarwal AK, Seltzer E, Merchant RM, et al. Tracking mental health and symptom mentions on Twitter during COVID-19. J Gen Intern Med. 2020;35(9):2798-2800. [FREE Full text] [CrossRef] [Medline]
    81. Keyes KM, Kandula S, Olfson M, Gould MS, Martínez-Alés G, Rutherford C, et al. Suicide and the agent-host-environment triad: leveraging surveillance sources to inform prevention. Psychol Med. 2021;51(4):529-537. [FREE Full text] [CrossRef] [Medline]
    82. Knipe D, Gunnell D, Evans H, John A, Fancourt D. Is Google trends a useful tool for tracking mental and social distress during a public health emergency? a time-series analysis. J Affect Disord. 2021;294:737-744. [FREE Full text] [CrossRef] [Medline]
    83. Lin YH, Chiang TW, Lin YL. Increased Internet searches for Insomnia as an indicator of global mental health during the COVID-19 pandemic: multinational longitudinal study. J Med Internet Res. 2020;22(9):e22181. [FREE Full text] [CrossRef] [Medline]
    84. Nason SL, Lin E, Eitzer B, Koelmel J, Peccia J. Changes in sewage sludge chemical signatures during a COVID-19 community lockdown, part 1: traffic, drugs, mental health, and disinfectants. Environ Toxicol Chem. 2022;41(5):1179. [FREE Full text] [CrossRef]
    85. Sycińska-Dziarnowska M, Szyszka-Sommerfeld L, Kłoda K, Simeone M, Woźniak K, Spagnuolo G. Mental health interest and its prediction during the COVID-19 pandemic using Google trends. Int J Environ Res Public Health. 2021;18(23):12369. [FREE Full text] [CrossRef] [Medline]
    86. Tsao SF, Chen H, Tisseverasinghe T, Yang Y, Li L, Butt ZA. What social media told us in the time of COVID-19: a scoping review. Lancet Digit Health. 2021;3(3):e175-e194. [FREE Full text] [CrossRef] [Medline]
    87. Melchor-Martínez EM, Jiménez-Rodríguez MG, Martínez-Ruiz M, Peña-Benavides SA, Iqbal HMN, Parra-Saldívar R, et al. Antidepressants surveillance in wastewater: overview extraction and detection. CSCEE. 2021;3:100074. [FREE Full text] [CrossRef]
    88. Erickson TB, Endo N, Duvallet C, Ghaeli N, Hess K, Alm EJ, et al. "Waste not, want not"—leveraging sewer systems and wastewater-based epidemiology for drug use trends and pharmaceutical monitoring. J Med Toxicol. 2021;17(4):397-410. [FREE Full text] [CrossRef] [Medline]
    89. Kilby AE, Denhart C. Location inference on social media data for agile monitoring of public health crises: an application to opioid use and abuse during the Covid-19 pandemic. ArXiv Preprint posted online on 02 Nov 2021. [FREE Full text]
    90. Lavertu A, Hamamsy T, Altman RB. Monitoring the opioid epidemic via social media discussions. medRxiv. Preprint posted online on April 07, 2021. [FREE Full text] [CrossRef]
    91. Slavova S, Rock P, Bush HM, Quesinberry D, Walsh SL. Signal of increased opioid overdose during COVID-19 from emergency medical services data. Drug Alcohol Depend. 2020;214:108176. [FREE Full text] [CrossRef] [Medline]
    92. Hou Z, Du F, Zhou X, Jiang H, Martin S, Larson H, et al. Cross-country comparison of public awareness, rumors, and behavioral responses to the COVID-19 epidemic: infodemiology study. J Med Internet Res. 2020;22(8):e21143. [FREE Full text] [CrossRef] [Medline]
    93. Mackey TK, Li J, Purushothaman V, Nali M, Shah N, Bardier C, et al. Big data, natural language processing, and deep learning to detect and characterize illicit COVID-19 product sales: infoveillance study on Twitter and Instagram. JMIR Public Health Surveill. 2020;6(3):e20794. [FREE Full text] [CrossRef] [Medline]
    94. Shahsavari S, Holur P, Wang T, Tangherlini TR, Roychowdhury V. Conspiracy in the time of corona: automatic detection of emerging COVID-19 conspiracy theories in social media and the news. J Comput Soc Sci. 2020;3(2):279-317. [FREE Full text] [CrossRef] [Medline]
    95. Shams AB, Apu EH, Rahman A, Raihan MMS, Siddika N, Preo RB, et al. Web Search Engine Misinformation Notifier Extension (SEMiNExt): a machine learning based approach during COVID-19 pandemic. Healthcare (Basel). 2021;9(2):156. [FREE Full text] [CrossRef] [Medline]
    96. Storeng KT, de Bengy Puyvallée A. The smartphone pandemic: how big tech and public health authorities partner in the digital response to Covid-19. Glob Public Health. 2021;16(8-9):1482-1498. [FREE Full text] [CrossRef] [Medline]
    97. Tan EY, Wee RR, Saw YE, Heng KJ, Chin JW, Tong EM, et al. Tracking private WhatsApp discourse about COVID-19 in Singapore: longitudinal infodemiology study. J Med Internet Res. 2021;23(12):e34218. [FREE Full text] [CrossRef] [Medline]
    98. Syrowatka A, Kuznetsova M, Alsubai A, Beckman AL, Bain PA, Craig KJT, et al. Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases. NPJ Digit Med. 2021;4(1):96. [FREE Full text] [CrossRef] [Medline]
    99. Kurita J, Sugawara T, Ohkusa Y. Interim evaluation of Google AI forecasting for COVID-19 compared with statistical forecasting by human intelligence in the first week. medRxiv. Preprint posted online on December 18, 2020. [FREE Full text] [CrossRef]
    100. Ramchandani A, Fan C, Mostafavi A. DeepCOVIDNet: an interpretable deep learning model for predictive surveillance of COVID-19 using heterogeneous features and their interactions. IEEE Access. 2020;8:159915-159930. [FREE Full text] [CrossRef] [Medline]
    101. Hansen LH, Rasmussen TL, Villesen P. Social compliance during high stringency periods efficiently reduces COVID-19 incidencevidence from Google mobility reports. Research Square. 2021 [FREE Full text] [CrossRef]
    102. Safranek CW, Scheinker D. A method to analyze rideshare data for the surveillance of novel strains of SARS-CoV-2. SSRN. 2021 [FREE Full text] [CrossRef]
    103. Zeng C, Zhang J, Li Z, Sun X, Olatosi B, Weissman S, et al. Spatial-temporal relationship between population mobility and COVID-19 outbreaks in South Carolina: time series forecasting analysis. J Med Internet Res. 2021;23(4):e27045. [FREE Full text] [CrossRef] [Medline]
    104. Szocska M, Pollner P, Schiszler I, Joo T, Palicz T, McKee M, et al. Countrywide population movement monitoring using mobile devices generated (big) data during the COVID-19 crisis. Sci Rep. 2021;11(1):5943. [FREE Full text] [CrossRef] [Medline]
    105. Sulyok M, Walker M. Community movement and COVID-19: a global study using Google's community mobility reports. Epidemiol Infect. 2020;148:e284. [FREE Full text] [CrossRef] [Medline]
    106. de Oliveira SB, Pôrto VBG, Ganem F, Mendes FM, Almiron M, de Oliveira WK, et al. Monitoring social distancing and SARS-CoV-2 transmission in Brazil using cell phone mobility data. medRxiv. Preprint posted online on May 05, 2020. [FREE Full text] [CrossRef]
    107. Verhulst S, Zahuranec AJ, Young A, Ramesh A. The use of mobility data for responding to the COVID-19 pandemic. SSRN. 2021 [CrossRef]
    108. Nageshwaran G, Harris RC, Guerche-Seblain CE. Review of the role of big data and digital technologies in controlling COVID-19 in Asia: public health interest vs. privacy. Digit Health. 2021;7:20552076211002953. [FREE Full text] [CrossRef] [Medline]
    109. Ribeiro-Navarrete S, Saura JR, Palacios-Marqués D. Towards a new era of mass data collection: assessing pandemic surveillance technologies to preserve user privacy. Technol Forecast Soc Change. 2021;167:120681. [FREE Full text] [CrossRef] [Medline]
    110. Kurian SJ, Bhatti AUR, Ting HH, Storlie C, Shah N, Bydon M. Utilization of mobility data in the fight against COVID-19. Mayo Clin Proc Innov Qual Outcomes. 2020;4(6):733-735. [FREE Full text] [CrossRef] [Medline]
    111. Saran S, Singh P, Kumar V, Chauhan P. Review of geospatial technology for infectious disease surveillance: use case on COVID-19. J Indian Soc Remote Sens. 2020;48(8):1121-1138. [FREE Full text] [CrossRef]
    112. Najah R. Surveillance technologies in the Covid-19 era. Policy Center For The New South. URL: https://www.policycenter.ma/publications/surveillance-technologies-covid-19-era [accessed 2023-12-23]
    113. Sonn JW, Lee JK. The smart city as time-space cartographer in COVID-19 control: the South Korean strategy and democratic control of surveillance technology. Eurasian Geography Econ. 2020;61(4-5):482-492. [CrossRef]
    114. Adeniyi EA, Awotunde JB, Ogundokun RO, Kolawole PO, Abiodun MK, Adeniyi AA. Mobile health application and COVID-19: opportunities and challenges. J Crit Rev. 2020;7(15):3481-3488. [FREE Full text]
    115. Kishore K, Jaswal V, Verma M, Koushal V. Exploring the utility of Google mobility data during the COVID-19 pandemic in India: digital epidemiological analysis. JMIR Public Health Surveill. 2021;7(8):e29957. [FREE Full text] [CrossRef] [Medline]
    116. Zheng Z, Xie Z, Qin Y, Wang K, Yu Y, Fu P. Exploring the influence of human mobility factors and spread prediction on early COVID-19 in the USA. BMC Public Health. 2021;21(1):615. [FREE Full text] [CrossRef] [Medline]
    117. Aljumah A. Assessment of machine learning techniques in IoT-based architecture for the monitoring and prediction of COVID-19. Electronics. 2021;10(15):1834. [FREE Full text] [CrossRef]
    118. Ahmed I, Ahmad M, Jeon G, Piccialli F. A framework for pandemic prediction using big data analytics. Big Data Research. 2021;25:100190. [FREE Full text] [CrossRef]
    119. Wang S, Ding S, Xiong L. A new system for surveillance and digital contact tracing for COVID-19: spatiotemporal reporting over network and GPS. JMIR Mhealth Uhealth. 2020;8(6):e19457. [FREE Full text] [CrossRef] [Medline]
    120. Budd J, Miller BS, Manning EM, Lampos V, Zhuang M, Edelstein M, et al. Digital technologies in the public-health response to COVID-19. Nat Med. 2020;26(8):1183-1192. [FREE Full text] [CrossRef] [Medline]
    121. Abd-Alrazaq A, Schneider J, Alhuwail D, Hamdi M, Al-Kuwari S, Al-Thani D, et al. Artificial intelligence in the fight against the COVID-19 pandemic: opportunities and challenges. Lecture Notes Bioeng. 2021:185-196. [CrossRef]
    122. Wang L, Ben X, Adiga A, Sadilek A, Tendulkar A, Venkatramanan S, et al. Using mobility data to understand and forecast COVID19 dynamics. medRxiv. Preprint posted online on December 15, 2020. [FREE Full text] [CrossRef] [Medline]
    123. Kostkova P, Saigí-Rubió F, Eguia H, Borbolla D, Verschuuren M, Hamilton C, et al. Data and digital solutions to support surveillance strategies in the context of the COVID-19 pandemic. Front Digit Health. 2021;3:707902. [FREE Full text] [CrossRef] [Medline]
    124. Golinelli D, Boetto E, Carullo G, Nuzzolese AG, Landini MP, Fantini MP. Adoption of digital technologies in health care during the COVID-19 pandemic: systematic review of early scientific literature. J Med Internet Res. 2020;22(11):e22280. [FREE Full text] [CrossRef] [Medline]
    125. Ahmed A, Boopathy P, Sudhagara RS. Artificial intelligence for the Novel Corona Virus (COVID-19) pandemic. Int J E-Health Med Commun. 2022;13(2):1-21. [FREE Full text] [CrossRef]
    126. Radanliev P, De Roure D, Walton R, Van Kleek M, Montalvo RM, Santos O, et al. COVID-19 what have we learned? The rise of social machines and connected devices in pandemic management following the concepts of predictive, preventive and personalized medicine. EPMA J. 2020;11(3):311-332. [FREE Full text] [CrossRef] [Medline]
    127. Rimpiläinen S, Thomas J, Morrison C. Global examples of COVID-19 surveillance technologies. Digital Health & Care Institute. 2020. URL: http://tinyurl.com/y995vc8x [accessed 2023-12-23]
    128. Zhang Q, Gao J, Wu JT, Cao Z, Zeng DD. Data science approaches to confronting the COVID-19 pandemic: a narrative review. Philos Trans A Math Phys Eng Sci. 2022;380(2214):20210127. [FREE Full text] [CrossRef] [Medline]
    129. Gunasekeran DV, Tseng RMWW, Tham YC, Wong TY. Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies. NPJ Digit Med. 2021;4(1):40. [FREE Full text] [CrossRef] [Medline]
    130. Mbunge E, Akinnuwesi B, Fashoto SG, Metfula AS, Mashwama P. A critical review of emerging technologies for tackling COVID-19 pandemic. Hum Behav Emerg Technol. 2021;3(1):25-39. [FREE Full text] [CrossRef] [Medline]
    131. Wu J, Wang J, Nicholas S, Maitland E, Fan Q. Application of big data technology for COVID-19 prevention and control in China: lessons and recommendations. J Med Internet Res. 2020;22(10):e21980. [FREE Full text] [CrossRef] [Medline]
    132. Mitze T, Rode J. Early-stage spatial disease surveillance of novel SARS-CoV-2 variants of concern in Germany with crowdsourced data. Sci Rep. 2022;12(1):899. [FREE Full text] [CrossRef] [Medline]
    133. McDonald DJ, Bien J, Green A, Hu AJ, DeFries N, Hyun S, et al. Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction? Proc Natl Acad Sci U S A. 2021;118(51):e2111453118. [FREE Full text] [CrossRef] [Medline]
    134. Mukka M, Pesälä S, Hammer C, Mustonen P, Jormanainen V, Pelttari H, et al. Analyzing citizens' and health care professionals' searches for smell/taste disorders and coronavirus in Finland during the COVID-19 pandemic: infodemiological approach using database logs. JMIR Public Health Surveill. 2021;7(12):e31961. [FREE Full text] [CrossRef] [Medline]
    135. Ma S, Yang S. COVID-19 forecasts using internet search information in the United States. Sci Rep. 2022;12:11539. [FREE Full text] [CrossRef]
    136. Fenton S, Peters CE, Quinn EK, Rydz E, Davies HW, MacPherson RA, et al. A media surveillance analysis of COVID-19 workplace outbreaks in Canada and the United States. Occup Environ Med. 2021;78(SUPPL 1):A99. [FREE Full text] [CrossRef]
    137. Paiva MG, Barroso TS, Miguel CJ, Marques R, Fernandes D, Portela D, et al. COVID-19 predictability in Portugal using Google trends. Eur J Public Health. 2021;31(suppl 3):iii341-iii342. [FREE Full text] [CrossRef]
    138. Ding Q, Massey D, Huang C, Grady CB, Lu Y, Cohen A, et al. Tracking self-reported symptoms and medical conditions on social media during the COVID-19 pandemic: infodemiological study. JMIR Public Health Surveill. 2021;7(9):e29413. [FREE Full text] [CrossRef] [Medline]
    139. Wang L, Li Z, Chen H, Ma H, Qiu S, Liu Y, et al. Early detection of COVID-19 epidemic using search engine query data. SSRN. 2021 [FREE Full text] [CrossRef]
    140. Rabiolo A, Alladio E, Morales E, McNaught AI, Bandello F, Afifi AA, et al. Forecasting the COVID-19 epidemic by integrating symptom search behavior into predictive models: infoveillance study. J Med Internet Res. 2021;23(8):e28876. [FREE Full text] [CrossRef] [Medline]
    141. Gil-Jardiné C, Chenais G, Pradeau C, Tentillier E, Revel P, Combes X, et al. Surveillance of COVID-19 using a keyword search for symptoms in reports from emergency medical communication centers in Gironde, France: a 15 year retrospective cross-sectional study. Intern Emerg Med. 2022;17(2):603-608. [FREE Full text] [CrossRef] [Medline]
    142. Husnayain A, Chuang TW, Fuad A, Su ECY. High variability in model performance of Google relative search volumes in spatially clustered COVID-19 areas of the USA. Int J Infect Dis. 2021;109:269-278. [FREE Full text] [CrossRef] [Medline]
    143. Zolbanin HM, Zadeh AH, Davazdahemami B. Miscommunication in the age of communication: a crowdsourcing framework for symptom surveillance at the time of pandemics. Int J Med Inform. 2021;151:104486. [FREE Full text] [CrossRef] [Medline]
    144. Abbas M, Morland TB, Hall ES, El-Manzalawy Y. Associations between Google search trends for symptoms and COVID-19 confirmed and death cases in the United States. Int J Environ Res Public Health. 2021;18(9):4560. [FREE Full text] [CrossRef] [Medline]
    145. Fulk A, Romero-Alvarez D, Abu-Saymeh Q, Onge JMS, Peterson AT, Agusto FB. Using Google health trends to investigate COVID-19 incidence in Africa. PLoS One. 2022;17(6):e0269573. [FREE Full text] [CrossRef] [Medline]
    146. Espinosa L, Wijermans A, Orchard F, Höhle M, Czernichow T, Coletti P, et al. Epitweetr: early warning of public health threats using Twitter data. Euro Surveill. 2022;27(39):2200177. [FREE Full text] [CrossRef] [Medline]
    147. Alvarez E, Obando D, Crespo S, Garcia E, Kreplak N, Marsico F. Estimating COVID-19 cases and outbreaks on-stream through phone calls. R Soc Open Sci. 2021;8(3):202312. [FREE Full text] [CrossRef] [Medline]
    148. Kogan NE, Clemente L, Liautaud P, Kaashoek J, Link NB, Nguyen AT, et al. An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time. Sci Adv. 2021;7(10):eabd6989. [FREE Full text] [CrossRef] [Medline]
    149. Lu T, Reis BY. Internet search patterns reveal clinical course of COVID-19 disease progression and pandemic spread across 32 countries. NPJ Digit Med. 2021;4(1):22. [FREE Full text] [CrossRef] [Medline]
    150. Hamoui BM, Alashaikh A, Alanazi E. Google searches and COVID-19 cases in Saudi Arabia: a correlation study. ArXiv. Preprint posted online on December 01, 2020. [FREE Full text] [CrossRef]
    151. Chandra M, Syakurah R. Potential use of personal protection online search during COVID-19 pandemic for predicting and monitoring public response. IJPHS. 2020;9(4):406. [FREE Full text] [CrossRef]
    152. Lopreite M, Panzarasa P, Puliga M, Riccaboni M. Early warnings of COVID-19 outbreaks across Europe from social media. Sci Rep. 2021;11(1):2147. [FREE Full text] [CrossRef] [Medline]
    153. Nsoesie EO, Sy KTL, Oladeji O, Sefala R, Nichols BE. Nowcasting and forecasting provincial-level SARS-CoV-2 case positivity using Google search data in South Africa. medRxiv. Preprint posted online on November 06, 2020. [FREE Full text] [CrossRef]
    154. Guo JW, Radloff CL, Wawrzynski SE, Cloyes KG. Mining Twitter to explore the emergence of COVID-19 symptoms. Public Health Nurs. 2020;37(6):934-940. [FREE Full text] [CrossRef] [Medline]
    155. Kurian SJ, Bhatti AUR, Alvi MA, Ting HH, Storlie C, Wilson PM, et al. Correlations between COVID-19 cases and Google trends data in the United States: a state-by-state analysis. Mayo Clin Proc. 2020;95(11):2370-2381. [FREE Full text] [CrossRef] [Medline]
    156. Cousins HC, Cousins CC, Harris A, Pasquale LR. Regional infoveillance of COVID-19 case rates: analysis of search-engine query patterns. J Med Internet Res. 2020;22(7):e19483. [FREE Full text] [CrossRef] [Medline]
    157. Peng Z, Wang R, Liu L, Wu H. Exploring urban spatial features of COVID-19 transmission in Wuhan based on social media data. IJGI. 2020;9(6):402. [FREE Full text] [CrossRef]
    158. Mavragani A. Tracking COVID-19 in Europe: infodemiology approach. JMIR Public Health Surveill. 2020;6(2):e18941. [FREE Full text] [CrossRef] [Medline]
    159. Ben S, Xin J, Chen S, Jiang Y, Yuan Q, Su L, et al. Global internet search trends related to gastrointestinal symptoms predict regional COVID-19 outbreaks. J Infect. 2022;84(1):56-63. [FREE Full text] [CrossRef] [Medline]
    160. Zhou Y, Jiang JY, Chen X, Wang W. #StayHome or #Marathon?: Social media enhanced pandemic surveillance on spatial-temporal dynamic graphs. Presented at: CIKM '21: The 30th ACM International Conference on Information and Knowledge Management; November 1-5, 2021, 2021;2738-2748; Queensland, Australia. [CrossRef]
    161. Santangelo OE, Provenzano S, Gianfredi V. Infodemiology of flu: Google trends-based analysis of Italians' digital behavior and a focus on SARS-CoV-2, Italy. J Prev Med Hyg. 2021;62(3):E586-E591. [FREE Full text] [CrossRef] [Medline]
    162. Ben S, Xin J, chen S, Christiani DC, Jiang Y, Yuan Q, et al. Real-time monitoring of the trends of GI symptoms searched on the internet effectively predicts the COVID-19 outbreak. SSRN J. Preprint posted online on August 18 2021. [FREE Full text] [CrossRef]
    163. Momynaliev KT, Khoroshun DK, Akimkin VG. Online queries as a criterion for evaluation of the epidemiological status and effectiveness of COVID-19 epidemic control measures. medRxiv. Preprint posted online on July 14 2021. [FREE Full text] [CrossRef]
    164. Whitfield C, Liu Y, Anwar M. Surveillance of COVID-19 pandemic using social media: a reddit study in North Carolina. Presented at: BCB '21: 12th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics; August 1-4, 2021, 2021;1-8; Gainesville, Florida. [CrossRef]
    165. Park GS, Bae J, Lee JH, Yun BY, Lee B, Shin EK. Integrated infodemic surveillance system: the case of COVID-19 in South Korea. Stud Health Technol Inform. 2021;281:1036-1040. [CrossRef] [Medline]
    166. Mangono T, Smittenaar P, Caplan Y, Huang VS, Sutermaster S, Kemp H, et al. Information-seeking patterns during the COVID-19 pandemic across the United States: longitudinal analysis of Google trends data. J Med Internet Res. 2021;23(5):e22933. [FREE Full text] [CrossRef] [Medline]
    167. Kelly-Reif K, Rinsky JL, Chiu SK, Burrer S, de Perio MA, Trotter AG, et al. Media reports as a tool for timely monitoring of COVID-19-related deaths among first responders-United States, April 2020. Public Health Rep. 2021;136(3):315-319. [FREE Full text] [CrossRef] [Medline]
    168. Huang W, Cao B, Yang G, Luo N, Chao N. Turn to the internet first? using online medical behavioral data to forecast COVID-19 epidemic trend. Inf Process Manag. 2021;58(3):102486. [FREE Full text] [CrossRef] [Medline]
    169. Sulyok M, Ferenci T, Walker M. Google trends data and COVID-19 in Europe: correlations and model enhancement are European wide. Transbound Emerg Dis. 2021;68(4):2610-2615. [FREE Full text] [CrossRef] [Medline]
    170. Hisada S, Murayama T, Tsubouchi K, Fujita S, Yada S, Wakamiya S, et al. Surveillance of early stage COVID-19 clusters using search query logs and mobile device-based location information. Sci Rep. 2020;10(1):18680. [FREE Full text] [CrossRef] [Medline]
    171. Younis J, Freitag H, Ruthberg JS, Romanes JP, Nielsen C, Mehta N. Social media as an early proxy for social distancing indicated by the COVID-19 reproduction number: observational study. JMIR Public Health Surveill. 2020;6(4):e21340. [FREE Full text] [CrossRef] [Medline]
    172. Li K, Liang Y, Li J, Liu M, Feng Y, Shao Y. Internet search data could be used as novel indicator for assessing COVID-19 epidemic. Infect Dis Model. 2020;5:848-854. [FREE Full text] [CrossRef] [Medline]
    173. Dai Y, Wang J. Identification of COVID-19 outbreak signals prior to the traditional disease surveillance system. SSRN J. Preprint posted online on May 08, 2020. [FREE Full text] [CrossRef]
    174. Valentin S, Mercier A, Lancelot R, Roche M, Arsevska E. Monitoring online media reports for early detection of unknown diseases: insight from a retrospective study of COVID-19 emergence. Transbound Emerg Dis. 2021;68(3):981-986. [FREE Full text] [CrossRef] [Medline]
    175. Venkatesh U, Gandhi PA. Prediction of COVID-19 outbreaks using Google trends in India: a retrospective analysis. Healthc Inform Res. 2020;26(3):175-184. [FREE Full text] [CrossRef] [Medline]
    176. Rajan A, Sharaf R, Brown RS, Sharaiha RZ, Lebwohl B, Mahadev S. Association of search query interest in gastrointestinal symptoms with COVID-19 diagnosis in the United States: infodemiology study. JMIR Public Health Surveill. 2020;6(3):e19354. [FREE Full text] [CrossRef] [Medline]
    177. Buonanno P, Puca M. Using newspaper obituaries to "nowcast" daily mortality: evidence from the Italian COVID-19 hot-spots. Health Policy. 2021;125(4):535-540. [FREE Full text] [CrossRef] [Medline]
    178. Higgins TS, Wu AW, Sharma D, Illing EA, Rubel K, Ting JY, et al. Snot Force Alliance. Correlations of online search engine trends with coronavirus disease (COVID-19) incidence: infodemiology study. JMIR Public Health Surveill. 2020;6(2):e19702. [FREE Full text] [CrossRef] [Medline]
    179. Cai M, Li J, Nali M, MacKey TK. Evaluation of hybrid unsupervised and supervised machine learning approach to detect self-reporting of COVID-19 symptoms on Twitter. Presented at: 2021 IEEE International Conference on Communications Workshops (ICC Workshops); June 14-23, 2021, 2021; Montreal, QC, Canada. [CrossRef]
    180. Liu Y, Whitfield C, Zhang T, Hauser A, Reynolds T, Anwar M. Monitoring COVID-19 pandemic through the lens of social media using natural language processing and machine learning. Health Inf Sci Syst. 2021;9(1):25. [FREE Full text] [CrossRef] [Medline]
    181. Elgazzar H, Spurlock K, Bogart T. Evolutionary clustering and community detection algorithms for social media health surveillance. Mach Learn Appl. 2021;6:100084. [FREE Full text] [CrossRef] [Medline]
    182. Alharbi A, Alosaimi W, Irfan UM. Automatic surveillance of pandemics using big data and text mining. Comput Mater Contin. 2021;68(1):303-317. [FREE Full text] [CrossRef]
    183. Lampos V, Majumder MS, Yom-Tov E, Edelstein M, Moura S, Hamada Y, et al. Tracking COVID-19 using online search. NPJ Digit Med. 2021;4(1):17. [FREE Full text] [CrossRef] [Medline]
    184. Lwowski B, Najafirad P. COVID-19 surveillance through Twitter using self-supervised and few shot learning. Presented at: Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020; December 2020, 2020; Virtual. URL: https://aclanthology.org/2020.nlpcovid19-2.9/ [CrossRef]
    185. Jiang JY, Zhou Y, Chen X, Jhou YR, Zhao L, Liu S, et al. COVID-19 surveiller: toward a robust and effective pandemic surveillance system based on social media mining. Philos Trans A Math Phys Eng Sci. 2022;380(2214):20210125. [FREE Full text] [CrossRef] [Medline]
    186. Peng Y, Li C, Rong Y, Pang CP, Chen X, Chen H. Real-time prediction of the daily incidence of COVID-19 in 215 countries and territories using machine learning: model development and validation. J Med Internet Res. 2021;23(6):e24285. [FREE Full text] [CrossRef] [Medline]
    187. Wen A, Wang L, He H, Liu S, Fu S, Sohn S, et al. An aberration detection-based approach for sentinel syndromic surveillance of COVID-19 and other novel influenza-like illnesses. J Biomed Inform. 2021;113:103660. [FREE Full text] [CrossRef] [Medline]
    188. Shen C, Chen A, Luo C, Zhang J, Feng B, Liao W. Using reports of symptoms and diagnoses on social media to predict covid-19 case counts in mainland China: observational infoveillance study. J Med Internet Res. 2020;22(5):e19421. [FREE Full text] [CrossRef] [Medline]
    189. Guo Y, Zhang Y, Lyu T, Prosperi M, Wang F, Xu H, et al. The application of artificial intelligence and data integration in COVID-19 studies: a scoping review. J Am Med Inform Assoc. 2021;28(9):2050-2067. [FREE Full text] [CrossRef] [Medline]
    190. Khemasuwan D, Colt HG. Applications and challenges of AI-based algorithms in the COVID-19 pandemic. BMJ Innov. 2021;7(2):387-398. [FREE Full text] [CrossRef]
    191. Bragazzi NL, Dai H, Damiani G, Behzadifar M, Martini M, Wu J. How big data and artificial intelligence can help better manage the COVID-19 pandemic. Int J Environ Res Public Health. 2020;17(9):3176. [FREE Full text] [CrossRef] [Medline]
    192. Urbaczewski A, Lee YJ. Information technology and the pandemic: a preliminary multinational analysis of the impact of mobile tracking technology on the COVID-19 contagion control. Eur J Inf Syst. 2020;29(4):405-414. [FREE Full text] [CrossRef]
    193. Weiß JP, Esdar M, Hübner U. Analyzing the essential attributes of nationally issued COVID-19 contact tracing apps: open-source intelligence approach and content analysis. JMIR Mhealth Uhealth. 2021;9(3):e27232. [FREE Full text] [CrossRef] [Medline]
    194. Wirth FN, Johns M, Meurers T, Prasser F. Citizen-centered mobile health apps collecting individual-level spatial data for infectious disease management: scoping review. JMIR Mhealth Uhealth. 2020;8(11):e22594. [FREE Full text] [CrossRef] [Medline]
    195. Chowdhury MJM, Ferdous MS, Biswas K, Chowdhury N, Muthukkumarasamy V. COVID-19 contact tracing: challenges and future directions. IEEE Access. 2020;8:225703-225729. [FREE Full text] [CrossRef] [Medline]
    196. Contact tracing for COVID-19: current evidence, options for scale-up and an assessment of resources needed. European Centre for Disease Prevention and Control. 2020. URL: http://tinyurl.com/4hyrfh6h [accessed 2023-12-23]
    197. Hurtado S, Marculescu R, Drake J, Srinivasan R. Pruning digital contact networks for meso-scale epidemic surveillance using foursquare data. Presented at: ASONAM '21: International Conference on Advances in Social Networks Analysis and Mining; November 8-11, 2021, 2021;423-430; Netherlands. URL: https://dl.acm.org/doi/abs/10.1145/3487351.3490971 [CrossRef]
    198. van Dijk WJ, Saadah NH, Numans ME, Aardoom JJ, Bonten TN, Brandjes M, et al. COVID RADAR app: description and validation of population surveillance of symptoms and behavior in relation to COVID-19. PLoS One. 2021;16(6):e0253566. [FREE Full text] [CrossRef] [Medline]
    199. Güemes A, Ray S, Aboumerhi K, Desjardins MR, Kvit A, Corrigan AE, et al. A syndromic surveillance tool to detect anomalous clusters of COVID-19 symptoms in the United States. Sci Rep. 2021;11(1):4660. [FREE Full text] [CrossRef] [Medline]
    200. Varsavsky T, Graham MS, Canas LS, Ganesh S, Pujol JC, Sudre CH, et al. Detecting COVID-19 infection hotspots in England using large-scale self-reported data from a mobile application: a prospective, observational study. medRxiv. Preprint posted online on November 17, 2020. [FREE Full text] [CrossRef] [Medline]
    201. Zens M, Brammertz A, Herpich J, Südkamp N, Hinterseer M. App-based tracking of self-reported COVID-19 symptoms: analysis of questionnaire data. J Med Internet Res. 2020;22(9):e21956. [FREE Full text] [CrossRef] [Medline]
    202. Mehl A, Bergey F, Cawley C, Gilsdorf A. Syndromic surveillance insights from a symptom assessment app before and during COVID-19 measures in Germany and the United Kingdom: results from repeated cross-sectional analyses. JMIR Mhealth Uhealth. 2020;8(10):e21364. [FREE Full text] [CrossRef] [Medline]
    203. Kennedy B, Fitipaldi H, Hammar U, Maziarz M, Tsereteli N, Oskolkov N, et al. App-based COVID-19 syndromic surveillance and prediction of hospital admissions in COVID symptom study Sweden. Nat Commun. 2022;13(1):2110. [FREE Full text] [CrossRef] [Medline]
    204. Sudre CH, Keshet A, Graham MS, Joshi AD, Shilo S, Rossman H, et al. Anosmia and other SARS-CoV-2 positive test-associated symptoms, across three national, digital surveillance platforms as the COVID-19 pandemic and response unfolded: an observation study. medRXiv. Preprint posted online on December 16, 2020. [FREE Full text] [CrossRef]
    205. Drew DA, Nguyen LH, Steves CJ, Menni C, Freydin M, Varsavsky T, et al. Rapid implementation of mobile technology for real-time epidemiology of COVID-19. Science. 2020;368(6497):1362-1367. [FREE Full text] [CrossRef] [Medline]
    206. Chan AT, Brownstein JS. Putting the public back in public health-surveying symptoms of Covid-19. N Engl J Med. 2020;383(7):e45. [FREE Full text] [CrossRef] [Medline]
    207. Canas LS, Sudre CH, Pujol JC, Polidori L, Murray B, Molteni E, et al. Early detection of COVID-19 in the UK using self-reported symptoms: a large-scale, prospective, epidemiological surveillance study. Lancet Digit Health. 2021;3(9):e587-e598. [FREE Full text] [CrossRef] [Medline]
    208. Yom-Tov E. Active syndromic surveillance of COVID-19 in Israel. Sci Rep. 2021;11(1):24449. [FREE Full text] [CrossRef] [Medline]
    209. Martin A, Nateqi J, Gruarin S, Munsch N, Abdarahmane I, Zobel M, et al. An artificial intelligence-based first-line defence against COVID-19: digitally screening citizens for risks via a chatbot. Sci Rep. 2020;10(1):19012. [FREE Full text] [CrossRef] [Medline]
    210. Yoneoka D, Tanoue Y, Kawashima T, Nomura S, Shi S, Eguchi A, et al. Large-scale epidemiological monitoring of the COVID-19 epidemic in Tokyo. Lancet Reg Health West Pac. 2020;3:100016. [FREE Full text] [CrossRef] [Medline]
    211. Maharaj AS, Parker J, Hopkins JP, Gournis E, Bogoch II, Rader B, et al. Comparison of longitudinal trends in self-reported symptoms and COVID-19 case activity in Ontario, Canada. PLoS One. 2022;17(1):e0262447. [FREE Full text] [CrossRef] [Medline]
    212. McDonald SA, Soetens LC, Schipper CMA, Friesema I, van den Wijngaard CC, Teirlinck A, et al. Testing behaviour and positivity for SARS-CoV-2 infection: insights from web-based participatory surveillance in the Netherlands. BMJ Open. 2021;11(12):e056077. [FREE Full text] [CrossRef] [Medline]
    213. Salomon JA, Reinhart A, Bilinski A, Chua EJ, La Motte-Kerr W, Rönn MM, et al. The US COVID-19 trends and impact survey: continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination. Proc Natl Acad Sci U S A. 2021;118(51):e2111454118. [FREE Full text] [CrossRef] [Medline]
    214. Runkle JD, Sugg MM, Graham G, Hodge B, March T, Mullendore J, et al. Participatory COVID-19 surveillance tool in rural appalachia : real-time disease monitoring and regional response. Public Health Rep. 2021;136(3):327-337. [FREE Full text] [CrossRef] [Medline]
    215. Ku D, Ku B, Leong T, Liu Z. Prevalence estimates of COVID-19 by web survey compared to inadequate testing. Research Square. 2020. URL: https://europepmc.org/article/ppr/ppr194290 [accessed 2023-12-23]
    216. Leal-Neto OB, Santos FAS, Lee JY, Albuquerque JO, Souza WV. Prioritizing COVID-19 tests based on participatory surveillance and spatial scanning. Int J Med Inform. 2020;143:104263. [FREE Full text] [CrossRef] [Medline]
    217. Hegde A, Masthi R, Krishnappa D. Hyperlocal postcode based crowdsourced surveillance systems in the COVID-19 pandemic response. Front Public Health. 2020;8(8):286. [FREE Full text] [CrossRef] [Medline]
    218. Luo H, Lie Y, Prinzen FW. Surveillance of COVID-19 in the general population using an online questionnaire: report from 18,161 respondents in China. JMIR Public Health Surveill. 2020;6(2):e18576. [FREE Full text] [CrossRef] [Medline]
    219. Wittwer S, Neto OBL, Paolotti D, Lichand G. Participatory surveillance for COVID-19 trends detection in Brazil. Research Square. 2022. URL: https://europepmc.org/article/ppr/ppr469519 [accessed 2023-12-23]
    220. Tozzi AE, Gesualdo F, Urbani E, Sbenaglia A, Ascione R, Procopio N, et al. Digital surveillance through an online decision support tool for COVID-19 over one year of the pandemic in Italy: observational study. J Med Internet Res. 2021;23(8):e29556. [FREE Full text] [CrossRef] [Medline]
    221. Reza M, Magen E, Vora N, Rogers K, Moll D, Warren E, et al. LB-14. CovidIQ- a text message-based symptom surveillance tracker that predicts new areas of increased incidence of Covid-19 disease. Open Forum Infect Dis. 2020;7(Supplement_1):S850-S851. [FREE Full text] [CrossRef]
    222. Astley CM, Tuli G, Mc Cord KA, Cohn EL, Rader B, Varrelman TJ, et al. Global monitoring of the impact of the COVID-19 pandemic through online surveys sampled from the Facebook user base. Proc Natl Acad Sci U S A. 2021;118(51):e2111455118. [FREE Full text] [CrossRef] [Medline]
    223. Gu H, Cheng SSM, Krishnan P, Ng DYM, Chang LDJ, Liu GYZ, et al. Monitoring international travelers arriving in Hong Kong for genomic surveillance of SARS-CoV-2. Emerg Infect Dis. 2022;28(1):247-250. [FREE Full text] [CrossRef] [Medline]
    224. Walker A, Houwaart T, Finzer P, Ehlkes L, Tyshaieva A, Damagnez M, et al. Characterization of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection clusters based on integrated genomic surveillance, outbreak analysis and contact tracing in an urban setting. Clin Infect Dis. 2022;74(6):1039. [FREE Full text] [CrossRef]
    225. Nicholls SM, Poplawski R, Bull MJ, Underwood A, Chapman M, Abu-Dahab K, et al. CLIMB-COVID: continuous integration supporting decentralised sequencing for SARS-CoV-2 genomic surveillance. Genome Biol. 2021;22:196. [FREE Full text] [CrossRef]
    226. van Dorp L, Houldcroft CJ, Richard D, Balloux F. COVID-19, the first pandemic in the post-genomic era. Curr Opin Virol. 2021;50:40-48. [FREE Full text] [CrossRef] [Medline]
    227. Willis S, Eberhardt K, Madoff L, DeMaria A, Brown CM, Madoff LC, et al. 1209. The evolving nature of syndromic surveillance during the COVID-19 pandemic in Massachusetts. Open Forum Infect Dis. 2021;8(Suppl 1):S695. [FREE Full text] [CrossRef]
    228. Boyle J, Sparks R. Syndromic surveillance to detect disease outbreaks using time between emergency department presentations. Emerg Med Australas. 2022;34(1):92-98. [FREE Full text] [CrossRef] [Medline]
    229. Boender TS, Cai W, Schranz M, Kocher T, Wagner B, Ullrich A, et al. Using routine emergency department data for syndromic surveillance of acute respiratory illness, Germany, week 10 2017 until week 10 2021. Euro Surveill. 2022;27(27):2100865. [FREE Full text] [CrossRef] [Medline]
    230. Postill G, Murray R, Wilton AS, Wells RA, Sirbu R, Daley MJ, et al. The use of cremation data for timely mortality surveillance during the COVID-19 pandemic in Ontario, Canada: validation study. JMIR Public Health Surveill. 2022;8(2):e32426. [FREE Full text] [CrossRef] [Medline]
    231. Bouchouar E, Hetman BM, Hanley B. Development and validation of an automated emergency department-based syndromic surveillance system to enhance public health surveillance in Yukon: a lower-resourced and remote setting. BMC Public Health. 2021;21(1):1247. [FREE Full text] [CrossRef] [Medline]
    232. Papadomanolakis-Pakis N, Maier A, van Dijk A, VanStone N, Moore KM. Development and assessment of a hospital admissions-based syndromic surveillance system for COVID-19 in Ontario, Canada: ACES pandemic tracker. BMC Public Health. 2021;21(1):1230. [FREE Full text] [CrossRef] [Medline]
    233. Stone M, Di Germanio C, Wright DJ, Sulaeman H, Dave H, Fink RV, et al. Use of US blood donors for national serosurveillance of severe acute respiratory syndrome coronavirus 2 antibodies: basis for an expanded national donor serosurveillance program. Clin Infect Dis. 2022;74(5):871-881. [FREE Full text] [CrossRef] [Medline]
    234. Ageron FX, Hugli O, Dami F, Caillet-Bois D, Pittet V, Eckert P, et al. Lessons from COVID-19 syndromic surveillance through emergency department activity: a prospective time series study from Western Switzerland. BMJ Open. 2022;12(5):e054504. [FREE Full text] [CrossRef] [Medline]
    235. Sayers DR, Hulse ST, Webber BJ, Burns TA, Denicoff AL. Notes from the field: use of emergency medical service data to augment COVID-19 public health surveillance in Montgomery County, Maryland, from march to june 2020. JMIR Public Health Surveill. 2020;6(3):e22331. [FREE Full text] [CrossRef] [Medline]
    236. Pulia MS, Hekman DJ, Glazer JM, Barclay-Buchanan C, Kuehnel N, Ross J, et al. Electronic health record-based surveillance for community transmitted COVID-19 in the emergency department. West J Emerg Med. 2020;21(4):748-751. [FREE Full text] [CrossRef] [Medline]
    237. Saeed S, Uzicanin S, Lewin A, Lieshout-Krikke R, Faddy H, Erikstrup C, et al. Current challenges of severe acute respiratory syndrome coronavirus 2 seroprevalence studies among blood donors: a scoping review. Vox Sang. 2022;117(4):476-487. [FREE Full text] [CrossRef] [Medline]
    238. Amman F, Markt R, Endler L, Hupfauf S, Agerer B, Schedl A, et al. Viral variant-resolved wastewater surveillance of SARS-CoV-2 at national scale. Nat Biotechnol. 2022:1814. [CrossRef]
    239. Lundy L, Fatta-Kassinos D, Slobodnik J, Karaolia P, Cirka L, Kreuzinger N, et al. Making waves: collaboration in the time of SARS-CoV-2 - rapid development of an international co-operation and wastewater surveillance database to support public health decision-making. Water Res. 2021;199:117167. [FREE Full text] [CrossRef] [Medline]
    240. Agrawal S, Orschler L, Schubert S, Zachmann K, Heijnen L, Tavazzi S, et al. Prevalence and circulation patterns of SARS-CoV-2 variants in European sewage mirror clinical data of 54 European cities. Water Res. 2022;214:118162. [CrossRef]
    241. Hart OE, Halden RU. Computational analysis of SARS-CoV-2/COVID-19 surveillance by wastewater-based epidemiology locally and globally: feasibility, economy, opportunities and challenges. Sci Total Environ. 2020;730:138875. [FREE Full text] [CrossRef] [Medline]
    242. Sharara N, Endo N, Duvallet C, Ghaeli N, Matus M, Heussner J, et al. Wastewater network infrastructure in public health: applications and learnings from the COVID-19 pandemic. PLOS Glob Public Health. 2021;1(12):e0000061. [FREE Full text] [CrossRef] [Medline]
    243. Mainardi PH, Bidoia ED. Challenges and emerging perspectives of an international SARS-CoV-2 epidemiological surveillance in wastewater. An Acad Bras Cienc. 2021;93(suppl 4):e20210163. [FREE Full text] [CrossRef] [Medline]
    244. Wade MJ, Jacomo AL, Armenise E, Brown MR, Bunce JT, Cameron GJ, et al. Understanding and managing uncertainty and variability for wastewater monitoring beyond the pandemic: lessons learned from the United Kingdom national COVID-19 surveillance programmes. J Hazard Mater. 2022;424(Pt B):127456. [FREE Full text] [CrossRef] [Medline]
    245. Shah S, Gwee SXW, Ng JQX, Lau N, Koh J, Pang J. Wastewater surveillance to infer COVID-19 transmission: a systematic review. Sci Total Environ. 2022;804:150060. [FREE Full text] [CrossRef] [Medline]
    246. McClary-Gutierrez JS, Mattioli MC, Marcenac P, Silverman AI, Boehm AB, Bibby K, et al. SARS-CoV-2 wastewater surveillance for public health action. Emerg Infect Dis. 2021;27(9):1-8. [FREE Full text] [CrossRef] [Medline]
    247. Peña-Guzmán C, Domínguez-Sánchez MA, Rodríguez M, Pulicharla R, Mora-Cabrera K. The urban water cycle as a planning tool to monitor SARS-CoV-2: a review of the literature. Sustainability. 2021;13(16):9010. [FREE Full text] [CrossRef]
    248. Amereh F, Negahban-Azar M, Isazadeh S, Dabiri H, Masihi N, Jahangiri-Rad M, et al. Sewage systems surveillance for SARS-CoV-2: identification of knowledge gaps, emerging threats, and future research needs. Pathogens. 2021;10(8):946. [FREE Full text] [CrossRef] [Medline]
    249. Pulicharla R, Kaur G, Brar SK. A year into the COVID-19 pandemic: rethinking of wastewater monitoring as a preemptive approach. J Environ Chem Eng. 2021;9(5):106063. [FREE Full text] [CrossRef] [Medline]
    250. Olesen SW, Imakaev M, Duvallet C. Making waves: defining the lead time of wastewater-based epidemiology for COVID-19. Water Res. 2021;202:117433. [FREE Full text] [CrossRef] [Medline]
    251. Berglund EZ, Thelemaque N, Spearing L, Faust KM, Kaminsky J, Sela L, et al. Water and wastewater systems and utilities: challenges and opportunities during the COVID-19 pandemic. J Water Resour Plann Manage. 2021;147(5):02521001. [CrossRef]
    252. Donia A, Hassan SU, Zhang X, Al-Madboly L, Bokhari H. COVID-19 crisis creates opportunity towards global monitoring and surveillance. Pathogens. 2021;10(3):256. [FREE Full text] [CrossRef] [Medline]
    253. Li X, Zhang S, Shi J, Luby SP, Jiang G. Uncertainties in estimating SARS-CoV-2 prevalence by wastewater-based epidemiology. Chem Eng J. 2021;415:129039. [FREE Full text] [CrossRef] [Medline]
    254. Tiwari SB, Gahlot P, Tyagi VK, Zhang L, Zhou Y, Kazmi AA, et al. Surveillance of Wastewater for Early Epidemic Prediction (SWEEP): environmental and health security perspectives in the post COVID-19 anthropocene. Environ Res. 2021;195:110831. [FREE Full text] [CrossRef] [Medline]
    255. Sharma VK, Jinadatha C, Lichtfouse E, Decroly E, van Helden J, Choi H, et al. COVID-19 epidemiologic surveillance using wastewater. Environ Chem Lett. 2021;19(3):1911-1915. [FREE Full text] [CrossRef] [Medline]
    256. Zhu Y, Oishi W, Maruo C, Saito M, Chen R, Kitajima M, et al. Early warning of COVID-19 via wastewater-based epidemiology: potential and bottlenecks. Sci Total Environ. 2021;767:145124. [FREE Full text] [CrossRef] [Medline]
    257. Medema G, Been F, Heijnen L, Petterson S. Implementation of environmental surveillance for SARS-CoV-2 virus to support public health decisions: opportunities and challenges. Curr Opin Environ Sci Health. 2020;17:49-71. [FREE Full text] [CrossRef] [Medline]
    258. Polo D, Quintela-Baluja M, Corbishley A, Jones DL, Singer AC, Graham DW, et al. Making waves: wastewater-based epidemiology for COVID-19—approaches and challenges for surveillance and prediction. Water Res. 2020;186:116404. [FREE Full text] [CrossRef] [Medline]
    259. Mandal P, Gupta AK, Dubey BK. A review on presence, survival, disinfection/removal methods of coronavirus in wastewater and progress of wastewater-based epidemiology. J Environ Chem Eng. 2020;8(5):104317. [FREE Full text] [CrossRef] [Medline]
    260. Dzinamarira T, Murewanhema G, Iradukunda PG, Madziva R, Herrera H, Cuadros DF, et al. Utilization of SARS-CoV-2 wastewater surveillance in Africa-a rapid review. Int J Environ Res Public Health. 2022;19(2):969. [FREE Full text] [CrossRef] [Medline]
    261. Hrudey SE, Conant B. The devil is in the details: emerging insights on the relevance of wastewater surveillance for SARS-CoV-2 to public health. J Water Health. 2022;20(1):246-270. [FREE Full text] [CrossRef] [Medline]
    262. Alhama J, Maestre JP, Martín MÁ, Michán C. Monitoring COVID-19 through SARS-CoV-2 quantification in wastewater: progress, challenges and prospects. Microb Biotechnol. 2022;15(6):1719-1728. [FREE Full text] [CrossRef] [Medline]
    263. de Lourdes Aguiar-Oliveira M, Campos A, Matos AR, Rigotto C, Sotero-Martins A, Teixeira PFP, et al. Wastewater-Based Epidemiology (WBE) and viral detection in polluted surface water: a valuable tool for COVID-19 surveillance-a brief review. Int J Environ Res Public Health. 2020;17(24):9251. [FREE Full text] [CrossRef] [Medline]
    264. Anand U, Li X, Sunita K, Lokhandwala S, Gautam P, Suresh S, et al. SARS-CoV-2 and other pathogens in municipal wastewater, landfill leachate, and solid waste: a review about virus surveillance, infectivity, and inactivation. Environ Res. 2022;203:111839. [FREE Full text] [CrossRef] [Medline]
    265. Corpuz MVA, Buonerba A, Vigliotta G, Zarra T, Ballesteros F, Campiglia P, et al. Viruses in wastewater: occurrence, abundance and detection methods. Sci Total Environ. 2020;745:140910. [FREE Full text] [CrossRef] [Medline]
    266. Bibby K, Bivins A, Wu Z, North D. Making waves: plausible lead time for wastewater based epidemiology as an early warning system for COVID-19. Water Res. 2021;202:117438. [FREE Full text] [CrossRef] [Medline]
    267. Hill K, Zamyadi A, Deere D, Vanrolleghem PA, Crosbie ND. SARS-CoV-2 known and unknowns, implications for the water sector and wastewater-based epidemiology to support national responses worldwidearly review of global experiences with the COVID-19 pandemic. Water Qual Res J. 2021;56(2):57-67. [FREE Full text] [CrossRef]
    268. Keshaviah A, Hu XC, Henry M. Developing a flexible national wastewater surveillance system for COVID-19 and beyond. Environ Health Perspect. 2021;129(4):45002. [FREE Full text] [CrossRef] [Medline]
    269. Ji B, Zhao Y, Wei T, Kang P. Water science under the global epidemic of COVID-19: bibliometric tracking on COVID-19 publication and further research needs. J Environ Chem Eng. 2021;9(4):105357. [FREE Full text] [CrossRef] [Medline]
    270. Anand U, Adelodun B, Pivato A, Suresh S, Indari O, Jakhmola S, et al. A review of the presence of SARS-CoV-2 RNA in wastewater and airborne particulates and its use for virus spreading surveillance. Environ Res. 2021;196:110929. [FREE Full text] [CrossRef] [Medline]
    271. Hamouda M, Mustafa F, Maraqa M, Rizvi T, Hassan AA. Wastewater surveillance for SARS-CoV-2: lessons learnt from recent studies to define future applications. Sci Total Environ. 2021;759:143493. [FREE Full text] [CrossRef] [Medline]
    272. Tetteh EK, Amankwa MO, Armah EK, Rathilal S. Fate of COVID-19 occurrences in wastewater systems: emerging detection and treatment technologies—a review. Water. 2020;12(10):2680. [FREE Full text] [CrossRef]
    273. Farkas K, Hillary LS, Malham SK, McDonald JE, Jones DL. Wastewater and public health: the potential of wastewater surveillance for monitoring COVID-19. Curr Opin Environ Sci Health. 2020;17:14-20. [FREE Full text] [CrossRef] [Medline]
    274. Abdeldayem OM, Dabbish AM, Habashy MM, Mostafa MK, Elhefnawy M, Amin L, et al. Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques: a comprehensive review and outlook. Sci Total Environ. 2022;803:149834. [FREE Full text] [CrossRef] [Medline]
    275. Curmi E, Fenner R, Richards K, Allwood JM, Bajželj B, Kopec GM. Visualising a stochastic model of californian water resources using sankey diagrams. Water Resour Manage. 2013;27(8):3035-3050. [CrossRef]
    276. Schmidt M. The sankey diagram in energy and material flow management. J Ind Ecol. 2008;12(2):173-185. [FREE Full text] [CrossRef]
    277. Lamer A, Laurent G, Pelayo S, Amrani ME, Chazard E, Marcilly R. Exploring patient path through sankey diagram: a proof of concept. Stud Health Technol Inform. 2020;270:218-222. [CrossRef] [Medline]
    278. Garritty C, Tricco AC, Smith M, Pollock D, Kamel C, King VJ, et al. Cochrane Rapid Reviews Methods Group. Rapid reviews methods series: involving patient and public partners, healthcare providers and policymakers as knowledge users. BMJ Evid Based Med. 2023 [FREE Full text] [CrossRef] [Medline]
    279. Concannon TW, Fuster M, Saunders T, Patel K, Wong JB, Leslie LK, et al. A systematic review of stakeholder engagement in comparative effectiveness and patient-centered outcomes research. J Gen Intern Med. 2014;29(12):1692-1701. [FREE Full text] [CrossRef] [Medline]
    280. The statement of principles for consumer involvement in Cochrane. Cochrane. 2017. URL: https://www.cochrane.org/news/statement-principles-consumer-involvement-cochrane [accessed 2023-12-23]
    281. Tomlinson E, Parker R. Six-step stakeholder engagement framework. Cochrane Community. 2021. URL: https://community.cochrane.org/news/six-step-stakeholder-engagement-framework [accessed 2023-12-23]
    282. Ahn PD, Wickramasinghe D. Pushing the limits of accountability: big data analytics containing and controlling COVID-19 in South Korea. Account Audit Account J. 2021;34(6):1320-1331. [CrossRef]
    283. Akhigbemen K, Bakare A, Bingham H, Crossland E, Cupac M, Jones M, et al. Do we support our governments to use strict surveillance methods in the fight against the coronavirus? J Glob Faultlines. 2020;7(1):101-103. [FREE Full text] [CrossRef]
    284. de Araujo Almeida B, Doneda D, Ichihara MY, Barral-Netto M, Matta GC, Rabello ET, et al. Personal data usage and privacy considerations in the COVID-19 global pandemic. Cien Saude Colet. 2020;25(Suppl 1):2487-2492. [FREE Full text] [CrossRef] [Medline]
    285. Andrei S. Coronavirus will pass, but Moscow's mass surveillance is here to stay. The Moscow Times. 2020. URL: http://tinyurl.com/4eyjyuh5 [accessed 2023-12-23]
    286. Bagchi K, Bannan C, Franklin SB, Hurlburt H, Sarkesian L, Schulman R, et al. Digital tools for COVID-19 contact tracing: identifying and mitigating the equity, privacy, and civil liberties concerns. World Health Organization. 2020. URL: https://pesquisa.bvsalud.org/global-literature-on-novel-coronavirus-2019-ncov/resource/en/grc-739713 [accessed 2023-12-23]
    287. do Carmo Barriga A, Martins AF, Simões MJ, Faustino D. The COVID-19 pandemic: yet another catalyst for governmental mass surveillance? Soc Sci Humanit Open. 2020;2(1):100096. [FREE Full text] [CrossRef]
    288. Berman G, Carter K, Garcia-Herranz M, Sekara V. Digital contact tracing and surveillance during COVID-19: general and child-specific ethical issues. UNICEF Office of Research. 2020. URL: https://www.un-ilibrary.org/content/papers/25206796/173/read [accessed 2023-12-23]
    289. Bernot A, Trauth-Goik A, Trevaskes S. Handling COVID-19 with big data in China: increasing ‘governance capacity’ or ‘function creep’? Aust J Int Aff. 2021;75(5):480-486. [CrossRef]
    290. Bhardwaj K. Digital surveillance systems to combat COVID-19 may do more harm than good. Econ Pol Wkly. 2020;55(23):1-10. [FREE Full text]
    291. Bradford L, Aboy M, Liddell K. COVID-19 contact tracing apps: a stress test for privacy, the GDPR, and data protection regimes. J Law Biosci. 2020;7(1):lsaa034. [FREE Full text] [CrossRef] [Medline]
    292. Calvo RA, Deterding S, Ryan RM. Health surveillance during COVID-19 pandemic. BMJ. 2020;369:m1373. [FREE Full text] [CrossRef] [Medline]
    293. Cofone I. Immunity passports and contact tracing surveillance. Stan Tech L Rev. 2021;24:176. [FREE Full text]
    294. Correia M, Rego G, Nunes R. The right to be forgotten and COVID-19: privacy versus public interest. Acta bioeth. 2021;27(1):59-67. [FREE Full text] [CrossRef]
    295. Couch DL, Robinson P, Komesaroff PA. COVID-19-extending surveillance and the panopticon. J Bioeth Inq. 2020;17(4):809-814. [FREE Full text] [CrossRef] [Medline]
    296. Cox D. Alarm bells ring for patient data and privacy in the COVID-19 goldrush. BMJ. 2020;369:m1925. [FREE Full text] [CrossRef] [Medline]
    297. Csernatoni R. New states of emergency: normalizing techno-surveillance in the time of COVID-19. Glob Aff. 2020;6(3):301-310. [CrossRef]
    298. Do Val da Fonseca M, de Carvalho Borges Kistmann VS. Design and COVID-19: monitoring urban data. Strateg Des Res J. 2021;14(1):289-298. [FREE Full text] [CrossRef]
    299. Eck K, Hatz S. State surveillance and the COVID-19 crisis. J Hum Rights. 2020;19(5):603-612. [FREE Full text] [CrossRef]
    300. Elgujja A, Arimoro A, Alshahrani FS, Barry M, Hersi A, Elgujja AA, et al. Mobile apps for COVID-19 surveillance: balancing public health needs with the privacy of personal data. SSRN J. Preprint posted online on October 30, 2021. [FREE Full text] [CrossRef]
    301. Foster MA. COVID-19, digital surveillance, and privacy: fourth amendment considerations. Congressional Research Service. 2020. URL: https://crsreports.congress.gov/product/pdf/LSB/LSB10449 [accessed 2023-12-23]
    302. Gasser U, Ienca M, Scheibner J, Sleigh J, Vayena E. Digital tools against COVID-19: taxonomy, ethical challenges, and navigation aid. Lancet Digit Health. 2020;2(8):e425-e434. [FREE Full text] [CrossRef] [Medline]
    303. Goldenfein J, Green B, Viljoen S. Privacy versus health is a false trade-off. SSRN J. Preprint posted online on May 26, 2020. [FREE Full text]
    304. Gómez-Ramírez O, Iyamu I, Ablona A, Watt S, Xu AXT, Chang HJ, et al. On the imperative of thinking through the ethical, health equity, and social justice possibilities and limits of digital technologies in public health. Can J Public Health. 2021;112(3):412-416. [FREE Full text] [CrossRef] [Medline]
    305. Greenleaf G, Kemp K. Australia's COVIDSafe experiment, phase II: a draft law for surveillance and trust. SSRN J. Preprint posted online on May 8, 2020. [FREE Full text]
    306. Greenleaf G, Kemp K. Police access to COVID check-in data is an affront to our privacy. we need stronger and more consistent rules in place. SSRN J. Preprint posted online on September 7, 2021. [FREE Full text] [CrossRef]
    307. Greitens SC. Surveillance, security, and liberal democracy in the post-COVID world. Int Org. 2020;74(S1):E169-E190. [FREE Full text] [CrossRef]
    308. Hendl T, Chung R, Wild V. Pandemic surveillance and racialized subpopulations: mitigating vulnerabilities in COVID-19 apps. J Bioeth Inq. 2020;17(4):829-834. [FREE Full text] [CrossRef] [Medline]
    309. Holmes EN, Linebaugh CD. COVID-19: digital contact tracing and privacy law. Congressional Research Service. 2020. URL: http://tinyurl.com/25eydkte [accessed 2023-12-23]
    310. Huddleston J. Could your smart phone help in the fight against the COVID-19 pandemic? American Action Forum. 2020. URL: http://tinyurl.com/4c5j5rbt [accessed 2023-12-23]
    311. Ienca M, Vayena E. On the responsible use of digital data to tackle the COVID-19 pandemic. Nat Med. 2020;26(4):463-464. [FREE Full text] [CrossRef] [Medline]
    312. Kampmark B. The pandemic surveillance state: an enduring legacy of COVID-19. J Glob Faultlines. 2020;7(1):59-70. [FREE Full text] [CrossRef]
    313. Khan A. Beyond consent: surveillance capitalism and politics in the data state. India Rev. 2021;20(2):158-175. [CrossRef]
    314. Kitchin R. Civil liberties or public health, or civil liberties and public health? using surveillance technologies to tackle the spread of COVID-19. Space Polity. 2020;24(3):362-381. [CrossRef]
    315. Nesterova I. The global flood of COVID-19 contact tracing apps: sailing with human rights and data protection standards against the wind of mass surveillance. SHS Web Conf. 2021;92:01035. [FREE Full text] [CrossRef]
    316. Kokal K, Subramanian V. Locking down on rights: surveillance and administrative ambiguity in the pandemic. Econ Pol Wkly. 2020;55(19):1-9. [FREE Full text]
    317. Kui S. The stumbling balance between public health and privacy amid the pandemic in China. Chinese J Comp Law. 2021;9(1):25-50. [FREE Full text] [CrossRef]
    318. Leonov O, Ablazov I. Cyber surveillance in terms of coronavirus pandemic. Pol Sci Secur Stud J. 2020;1(1):22-26. [FREE Full text] [CrossRef]
    319. Marquez CM, Ramirez JAO. COVID-19 and the challenges of digital surveillance for human rights: analysis of the app DataCOVID foreseen in the ministerial order SND/29/2020, of march 27th. Rev Bioetica Derecho. 2020(50):205-220. [FREE Full text]
    320. Švedkauskas Ž, Maati A. Long-term prescription? digital surveillance is here to stay. Czech J Int Relations. 2021;56(4):105-118. [CrossRef]
    321. Mann M, Mitchell P, Foth M. Pandemic tech Utopias and capitalist surveillance cultures: the case of privacy-preserving contact-tracing apps. SSRN J. Preprint posted online on June 7, 2022. [FREE Full text] [CrossRef]
    322. Mavriki P, Karyda M. Big data analytics in healthcare applications: privacy implications for individuals and groups and mitigation strategies. In: Themistocleous M, Papadaki M, Kamal MM, editors. Information Systems: 17th European, Mediterranean, and Middle Eastern Conference, EMCIS 2020, Dubai, United Arab Emirates, November 25–26, 2020, Proceedings, LNBIP 402. Cham, Switzerland. Springer International Publishing; 2020;526-540.
    323. McGrail KM. Contact-tracing apps: time to confront broader societal change. Am J Public Health. 2021;111(3):369-370. [CrossRef] [Medline]
    324. Miller S, Smith M. Ethics, public health and technology responses to COVID-19. Bioethics. 2021;35(4):366-371. [FREE Full text] [CrossRef] [Medline]
    325. Oliva JD. Surveillance, privacy, and app tracking. SSRN J. Preprint posted online on July 31, 2020. [FREE Full text]
    326. Oliva J. Public health surveillance in the context of COVID-19. Indiana Health Law Rev. 2021;18(1):107-122. [FREE Full text] [CrossRef]
    327. Roberts SL. COVID-19: the controversial role of big tech in digital surveillance. LSE Business Review. 2020. URL: http://tinyurl.com/33njhk5b [accessed 2023-12-23]
    328. Rule JB, Cheng H. Coronavirus and the surveillance state. Dissent. 2020;67(3):100-102. [CrossRef]
    329. Seto E, Challa P, Ware P. Adoption of COVID-19 contact tracing apps: a balance between privacy and effectiveness. J Med Internet Res. 2021;23(3):e25726. [FREE Full text] [CrossRef] [Medline]
    330. Sharon T. Blind-sided by privacy? digital contact tracing, the Apple/Google API and big tech's newfound role as global health policy makers. Ethics Inf Technol. 2021;23(Suppl 1):45-57. [FREE Full text] [CrossRef] [Medline]
    331. Shen T, Wang C. Big data technology applications and the right to health in China during the COVID-19 pandemic. Int J Environ Res Public Health. 2021;18(14):7325. [FREE Full text] [CrossRef] [Medline]
    332. Subbian V, Solomonides A, Clarkson M, Rahimzadeh VN, Petersen C, Schreiber R, et al. Ethics and informatics in the age of COVID-19: challenges and recommendations for public health organization and public policy. J Am Med Inform Assoc. 2021;28(1):184-189. [FREE Full text] [CrossRef] [Medline]
    333. Tewari A. Confronting Agamben on the pandemic: biopolitics, class struggle and surveillance capitalism. Law Cult Humanities. 2021 [CrossRef]
    334. Timotijevic J. Society's ‘new normal’? the role of discourse in surveillance and silencing of dissent during and post COVID-19. SSRN J. Preprint posted online on May 27, 2020. [FREE Full text] [CrossRef]
    335. Tromblay DE. Botching bio-surveillance: the department of homeland security and COVID-19 pandemic. Int J Intell Counterintell. 2021;35(1):164-167. [CrossRef]
    336. Unger W. Katz and COVID-19: how a pandemic changed the reasonable expectation of privacy. SSRN J. Preprint posted online on September 14, 2020. [FREE Full text] [CrossRef]
    337. Venkatasubramanian A. The human rights challenges of digital COVID-19 surveillance. Health Hum Rights. 2020;22(2):79-84. [FREE Full text] [Medline]
    338. Vitak J, Zimmer M. More than just privacy: using contextual integrity to evaluate the long-term risks from COVID-19 surveillance technologies. Soc Media Soc. 2020;6(3):1-4. [FREE Full text] [CrossRef] [Medline]
    339. Wee A, Findlay MJ. AI and data use: surveillance technology and community disquiet in the age of COVID-19. SSRN Journal. Preprint posted online on September 14, 2020. [FREE Full text] [CrossRef]
    340. Yang Y, Baik JS, Ahn SY, Jang E. Tracing digital contact tracing: surveillance technology and privacy rights during COVID-19 in China, South Korea, and the United States. SSRN J. Preprint posted online on November 6, 2020. [FREE Full text] [CrossRef]
    341. Yu A. Digital surveillance in post-coronavirus China: a feminist view on the price we pay. Gend Work Organ. 2020;27(5):774-777. [FREE Full text] [CrossRef] [Medline]
    342. Yuniarti S, Ratnaningsih E. Combating COVID-19: challenge for data protection and privacy. IOP Conf Ser Earth Environ Sci. 2021;729(1):012115. [FREE Full text] [CrossRef]
    343. Zhao IY, Ma YX, Yu MWC, Liu J, Dong WN, Pang Q, et al. Ethics, integrity, and retributions of digital detection surveillance systems for infectious diseases: systematic literature review. J Med Internet Res. 2021;23(10):e32328. [FREE Full text] [CrossRef] [Medline]
    344. Zinn Z. Surveillance and the 'new normal' of COVID-19: public health, data, and justice. Social Science Research Council. New York.; 2021. URL: http://tinyurl.com/vpwjaash [accessed 2023-12-23]
    345. Xu A. But what if big brother's surveillance saves lives?-comparative digital privacy in the time of coronavirus. Creighton L Rev. 2020;54(1):147-172.
    346. Bentotahewa V, Hewage C, Williams J. Solutions to big data privacy and security challenges associated with COVID-19 surveillance systems. Front Big Data. 2021;4:645204. [FREE Full text] [CrossRef] [Medline]
    347. Cassiano MS, Haggerty K, Bernot A. China's response to the COVID-19 pandemic: surveillance and autonomy. Surveill Soc. 2021;19(1):94-97. [FREE Full text] [CrossRef]
    348. Frith J, Saker M. It is all about location: smartphones and tracking the spread of COVID-19. Soc Media Soc. 2020;6(3):2056305120948257. [FREE Full text] [CrossRef] [Medline]
    349. Mesarcík M. Digital surveillance in the times of COVID-19: lessons from slovakia. Eur J Transform Stud. 2020;8:184-206. [FREE Full text]
    350. Hussein MR, Apu EH, Shahabuddin S, Shams AB, Kabir R. Overview of digital health surveillance system during COVID-19 pandemic: public health issues and misapprehensions. ArXiv. Preprint posted online on July 27, 2020. [FREE Full text]
    351. Hussein MR, Shams AB, Apu EH, Mamun KAA, Rahman MS. Digital surveillance systems for tracing COVID-19: privacy and security challenges with recommendations. ArXiv. Preprint posted online on July 26, 2020. [FREE Full text]
    352. Ramjee D, Sanderson P, Malek I. COVID-19 and digital contact tracing: regulating the future of public health surveillance. SSRN J. Preprint posted online on November 22, 2020. [FREE Full text] [CrossRef]
    353. Cong W. From pandemic control to data-driven governance: the case of China's health code. Front Polit Sci. 2021;3:627959. [FREE Full text] [CrossRef]
    354. Pawlotsky A. L'utilisation des données personnelles dans le cadre de la lutte contre l'épidémie de Covid-19, un risque pour les droits et libertés? Revue des droits de l'homme. 2020 [FREE Full text] [CrossRef]
    355. Ryan M. In defence of digital contact-tracing: human rights, South Korea and Covid-19. Int J Pervasive Comput Commun. 2020;16(4):383-407. [FREE Full text] [CrossRef]
    356. Veena D. The expansion of mass surveillance to stop coronavirus should worry us all. The Guardian. 2020. URL: http://tinyurl.com/37jndm86 [accessed 2023-12-23]
    357. Li TC. Privacy in pandemic: law, technology, and public health in the COVID-19 crisis. Loy U Chi L J. 2021;52(3):767-865. [FREE Full text] [CrossRef]
    358. Liu J, Zhao H. Privacy lost: appropriating surveillance technology in China's fight against COVID-19. Bus Horiz. 2021;64(6):743-756. [FREE Full text] [CrossRef] [Medline]
    359. Lucivero F, Hallowell N, Johnson S, Prainsack B, Samuel G, Sharon T. COVID-19 and contact tracing apps: technological fix or social experiment? SSRN J. Preprint posted online on May 24, 2020. [FREE Full text] [CrossRef]
    360. Madianou M. A second-order disaster? digital technologies during the COVID-19 pandemic. Soc Media Soc. 2020;6(3):2056305120948168. [FREE Full text] [CrossRef] [Medline]
    361. Mello MM, Wang CJ. Ethics and governance for digital disease surveillance. Science. 2020;368(6494):951-954. [FREE Full text] [CrossRef] [Medline]
    362. Nabben K. Trustless approaches to digital infrastructure in the crisis of COVID-19: Australia's newest COVID app, home-grown surveillance technologies and what to do about it. SSRN J. Preprint posted online on April 21, 2020. [FREE Full text] [CrossRef]
    363. Newlands G, Lutz C, Tamò-Larrieux A, Villaronga EF, Harasgama R, Scheitlin G. Innovation under pressure: implications for data privacy during the Covid-19 pandemic. Big Data Soc. 2020;7(2):205395172097668. [FREE Full text] [CrossRef]
    364. Ngan OMY, Kelmenson AM. Using big data tools to analyze digital footprint in the COVID-19 pandemic: some public health ethics considerations. Asia Pac J Public Health. 2021;33(1):129-130. [CrossRef] [Medline]
    365. Ram N, Gray D. Mass surveillance in the age of COVID-19. J Law Biosci. 2020;7(1):lsaa023. [FREE Full text] [CrossRef] [Medline]
    366. Raskar R, Schunemann I, Barbar R, Vilcans K, Gray J, Vepakomma P, et al. Apps gone rogue: maintaining personal privacy in an epidemic. ArXiv. Preprint posted online on March 19, 2020. [FREE Full text]
    367. Roberts SL. Tracking Covid-19 using big data and big tech: a digital Pandora's box. LSE British Politics and Policy. 2020. URL: https://blogs.lse.ac.uk/politicsandpolicy/tracking-covid-19/ [accessed 2023-12-23]
    368. Rozenshtein AZ. Digital disease surveillance. SSRN J. Preprint posted online on October 9, 2020. [FREE Full text] [CrossRef]
    369. Sekalala S, Dagron S, Forman L, Meier BM. Analyzing the human rights impact of increased digital public health surveillance during the COVID-19 crisis. Health Hum Rights. 2020;22(2):7-20. [FREE Full text] [Medline]
    370. Spears JL, Padyab A. Privacy risk in contact tracing systems. Behav Inform Technol. 2021;42(2):174-195. [CrossRef]
    371. Surber RS. Corona pan(dem)ic: gateway to global surveillance. Ethics Inf Technol. 2021;23(3):569-578. [FREE Full text] [CrossRef] [Medline]
    372. Scassa T, Millar J, Bronson K. Privacy, ethics, and contact-tracing apps. SSRN J. Preprint posted online on July 24, 2020. [FREE Full text] [CrossRef]
    373. von Struensee S. Mapping artificial intelligence applications deployed against COVID-19 alongside ethics and human rights considerations. SSRN J. Preprint posted online on July 26, 2021. [FREE Full text] [CrossRef]
    374. Mahapatra S. Digital surveillance and the threat to civil liberties in India. GIGA Focus Asia. 2021(3):1-12. [FREE Full text]
    375. Dawe V, Hardie J. The rise of the big data doctor: recent advances and warnings in digital public health technology. Electron J Gen Med. 2021;18(3):em284. [FREE Full text] [CrossRef]
    376. Dayalani H, Feistel K, Sanchez R, Denton SW, DeNardo MA, Boudreaux B. Data Privacy During Pandemics: A Scorecard Approach for Evaluating the Privacy Implications of COVID-19 Mobile Phone Surveillance Programs. Santa Monica, CA. RAND Corporation; 2020.
    377. Bariffi FJ, Puaschunder JM. Artificial intelligence and big data in the age of COVID-19. SSRN J. Preprint posted online on December 9, 2021. [FREE Full text] [CrossRef]
    378. Zarra A, Favalli S, Ceron M. Pandemic-sanctioned AI surveillance: human rights under the threat of algorithmic injustice in the EU. SSRN J. Preprint posted online on October 11, 2021. [FREE Full text] [CrossRef]
    379. Findlay M, Loke JY, Remolina N, Tham B. Ethics, AI, mass data and pandemic challenges: responsible data use and infrastructure application for surveillance and pre-emptive tracing post-crisis. SSRN J. Preprint posted online on May 4, 2020. [FREE Full text] [CrossRef]
    380. Findlay M, Remolina N. Regulating personal data usage in COVID-19 control conditions. SSRN J. Preprint posted online on May 22, 2020. [FREE Full text] [CrossRef]
    381. Leslie D. Tackling COVID-19 through responsible AI innovation: five steps in the right direction. SSRN J. Preprint posted online on July 23 2020. 2020 [FREE Full text] [CrossRef]
    382. Shachar C, Gerke S, Adashi EY. AI surveillance during pandemics: ethical implementation imperatives. Hastings Cent Rep. 2020;50(3):18-21. [FREE Full text] [CrossRef] [Medline]
    383. Gable L, Ram N, Ram JL. Legal and ethical implications of wastewater monitoring of SARS-CoV-2 for COVID-19 surveillance. J Law Biosci. 2020;7(1):lsaa039. [FREE Full text] [CrossRef] [Medline]
    384. Honda R, Murakami M, Hata A, Ihara M. Public health benefits and ethical aspects in the collection and open sharing of wastewater-based epidemic data on COVID-19. Data S J. 2021;20(1):1-6. [FREE Full text] [CrossRef]
    385. Hrudey SE, Silva DS, Shelley J, Pons W, Isaac-Renton J, Chik AH, et al. Ethics guidance for environmental scientists engaged in surveillance of wastewater for SARS-CoV-2. Environ Sci Technol. 2021;55(13):8484-8491. [FREE Full text] [CrossRef] [Medline]
    386. Joh EE. COVID-19 sewage testing as a police surveillance infrastructure. SSRN J. Preprint posted online on December 18, 2020. [FREE Full text] [CrossRef]
    387. Kintziger KW, Stone KW, Jagger MA, Horney JA. The impact of the COVID-19 response on the provision of other public health services in the U.S.: a cross sectional study. PLoS One. 2021;16(10):e0255844. [FREE Full text] [CrossRef] [Medline]
    388. Carroll WD, Strenger V, Eber E, Porcaro F, Cutrera R, Fitzgerald DA, et al. European and United Kingdom COVID-19 pandemic experience: the same but different. Paediatr Respir Rev. 2020;35:50-56. [FREE Full text] [CrossRef] [Medline]
    389. Onyeaka H, Anumudu CK, Al-Sharify ZT, Egele-Godswill E, Mbaegbu P. COVID-19 pandemic: a review of the global lockdown and its far-reaching effects. Sci Prog. 2021;104(2):368504211019854. [FREE Full text] [CrossRef] [Medline]
    390. Saraswat R, Saraswat DA. Research opportunities in pandemic lockdown. Science. 2020;368(6491):594-595. [FREE Full text] [CrossRef] [Medline]
    391. Grimshaw JM, Tovey DI, Lavis JN. COVID-END: an international network to better co-ordinate and maximize the impact of the global evidence synthesis and guidance response to COVID-19. Cochrane Database Syst Rev. 2020;12:4-8. [FREE Full text] [CrossRef]
    392. Homolak J, Kodvanj I, Virag D. Preliminary analysis of COVID-19 academic information patterns: a call for open science in the times of closed borders. Scientometrics. 2020;124(3):2687-2701. [FREE Full text] [CrossRef] [Medline]
    393. Sullivan P, Trapido E, Acquavella J, Gillum RF, Kirby RS, Kramer MR, et al. Editorial priorities and timeliness of editorial assessment and peer review during the COVID-19 pandemic. Ann Epidemiol. 2022;69:24-26. [FREE Full text] [CrossRef] [Medline]
    394. Dash S, Shakyawar SK, Sharma M, Kaushik S. Big data in healthcare: management, analysis and future prospects. J Big Data. 2019;6(1):54. [FREE Full text] [CrossRef]
    395. Mavragani A. Infodemiology and infoveillance: scoping review. J Med Internet Res. 2020;22(4):e16206. [FREE Full text] [CrossRef] [Medline]
    396. Liu P, Ibaraki M, VanTassell J, Geith K, Cavallo M, Kann R, et al. A sensitive, simple, and low-cost method for COVID-19 wastewater surveillance at an institutional level. Sci Total Environ. 2022;807(Pt 3):151047. [FREE Full text] [CrossRef] [Medline]
    397. Zhang Y, Zhu K, Huang W, Guo Z, Jiang S, Zheng C, et al. Can wastewater surveillance assist China to cost-effectively prevent the nationwide outbreak of COVID-19? Sci Total Environ. 2022;829:154719. [FREE Full text] [CrossRef] [Medline]
    398. Shrestha S, Yoshinaga E, Chapagain SK, Mohan G, Gasparatos A, Fukushi K. Wastewater-based epidemiology for cost-effective mass surveillance of COVID-19 in low- and middle-income countries: challenges and opportunities. Water. 2021;13(20):2897. [FREE Full text] [CrossRef]
    399. Nelson B. What poo tells us: wastewater surveillance comes of age amid Covid, Monkeypox, and Polio. BMJ. 2022;378:o1869. [FREE Full text] [CrossRef] [Medline]
    400. United States confirmed as country with circulating vaccine-derived poliovirus. Centers for Disease Control and Prevention. 2022. URL: http://tinyurl.com/5n9xukc9 [accessed 2023-12-23]
    401. Statement from the chief public health officer of Canada on June 7, 2021. Public Health Agency of Canada. 2021. URL: http://tinyurl.com/yc8v89vv [accessed 2023-12-23]
    402. Bathina KC, Thij MT, Valdez D, Rutter LA, Bollen J. Declining well-being during the COVID-19 pandemic reveals US social inequities. PLoS One. 2021;16(7):e0254114. [FREE Full text] [CrossRef] [Medline]
    403. Verduzco-Gutierrez M, Lara AM, Annaswamy TM. When disparities and disabilities collide: inequities during the COVID-19 pandemic. PM R. 2021;13(4):412-414. [FREE Full text] [CrossRef] [Medline]
    404. Marmot M, Allen J. COVID-19: exposing and amplifying inequalities. J Epidemiol Community Health. 2020;74(9):681-682. [FREE Full text] [CrossRef] [Medline]
    405. Chiriboga D, Garay J, Buss P, Madrigal RS, Rispel LC. Health inequity during the COVID-19 pandemic: a cry for ethical global leadership. Lancet. 2020;395(10238):1690-1691. [FREE Full text] [CrossRef] [Medline]
    406. Slemon A, Richardson C, Goodyear T, Salway T, Gadermann A, Oliffe JL, et al. Widening mental health and substance use inequities among sexual and gender minority populations: findings from a repeated cross-sectional monitoring survey during the COVID-19 pandemic in Canada. Psychiatry Res. 2022;307:114327. [FREE Full text] [CrossRef] [Medline]
    407. Alberti PM, Lantz PM, Wilkins CH. Equitable pandemic preparedness and rapid response: lessons from COVID-19 for pandemic health equity. J Health Polit Policy Law. 2020;45(6):921-935. [FREE Full text] [CrossRef] [Medline]
    408. Geneviève LD, Martani A, Wangmo T, Elger BS. Precision public health and structural racism in the United States: promoting health equity in the COVID-19 pandemic response. JMIR Public Health Surveill. 2022;8(3):e33277. [FREE Full text] [CrossRef] [Medline]
    409. Brush BL, Mentz G, Jensen M, Jacobs B, Saylor KM, Rowe Z, et al. Success in long-standing Community-Based Participatory Research (CBPR) partnerships: a scoping literature review. Health Educ Behav. 2020;47(4):556-568. [FREE Full text] [CrossRef] [Medline]

    ‎
    AI: artificial intelligence
    PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews

    Edited by A Mavragani, T Sanchez; submitted 23.05.23; peer-reviewed by D Paradice, I Mircheva; comments to author 18.08.23; revised version received 06.09.23; accepted 07.12.23; published 19.01.24.

    Copyright

    ©Emily C Clark, Sophie Neumann, Stephanie Hopkins, Alyssa Kostopoulos, Leah Hagerman, Maureen Dobbins. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 19.01.2024.

    This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on https://publichealth.jmir.org, as well as this copyright and license information must be included.