Published on 09.01.18 in Vol 4, No 1 (2018): Jan-Mar
Works citing "Accurate Influenza Monitoring and Forecasting Using Novel Internet Data Streams: A Case Study in the Boston Metropolis"
According to Crossref, the following articles are citing this article (DOI 10.2196/publichealth.8950):
(note that this is only a small subset of citations)
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Viboud C, Santillana M. Fitbit-informed influenza forecasts. The Lancet Digital Health 2020;2(2):e54
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Koehlmoos TP, Janvrin ML, Korona-Bailey J, Madsen C, Sturdivant R. COVID-19 Self-Reported Symptom Tracking Programs in the United States: Framework Synthesis. Journal of Medical Internet Research 2020;22(10):e23297
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Tana JC, Kettunen J, Eirola E, Paakkonen H. Diurnal Variations of Depression-Related Health Information Seeking: Case Study in Finland Using Google Trends Data. JMIR Mental Health 2018;5(2):e43
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Mavragani A, Ochoa G. Infoveillance of infectious diseases in USA: STDs, tuberculosis, and hepatitis. Journal of Big Data 2018;5(1)
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Edo-Osagie O, De La Iglesia B, Lake I, Edeghere O. A scoping review of the use of Twitter for public health research. Computers in Biology and Medicine 2020;122:103770
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Syamsuddin M, Fakhruddin M, Sahetapy-Engel JTM, Soewono E. Causality Analysis of Google Trends and Dengue Incidence in Bandung, Indonesia With Linkage of Digital Data Modeling: Longitudinal Observational Study. Journal of Medical Internet Research 2020;22(7):e17633
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Cheng H, Wu Y, Lin M, Liu Y, Tsai Y, Wu J, Pan K, Ke C, Chen C, Liu D, Lin I, Chuang J. Applying Machine Learning Models with An Ensemble Approach for Accurate Real-Time Influenza Forecasting in Taiwan: Development and Validation Study. Journal of Medical Internet Research 2020;22(8):e15394
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Hegde A, Masthi R, Krishnappa D. Hyperlocal Postcode Based Crowdsourced Surveillance Systems in the COVID-19 Pandemic Response. Frontiers in Public Health 2020;8
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Leal Neto O, Cruz O, Albuquerque J, Nacarato de Sousa M, Smolinski M, Pessoa Cesse E, Libel M, Vieira de Souza W. Participatory Surveillance Based on Crowdsourcing During the Rio 2016 Olympic Games Using the Guardians of Health Platform: Descriptive Study. JMIR Public Health and Surveillance 2020;6(2):e16119
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Mavragani A, Ochoa G, Tsagarakis KP. Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review. Journal of Medical Internet Research 2018;20(11):e270
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Lu FS, Hattab MW, Clemente CL, Biggerstaff M, Santillana M. Improved state-level influenza nowcasting in the United States leveraging Internet-based data and network approaches. Nature Communications 2019;10(1)
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Lutz CS, Huynh MP, Schroeder M, Anyatonwu S, Dahlgren FS, Danyluk G, Fernandez D, Greene SK, Kipshidze N, Liu L, Mgbere O, McHugh LA, Myers JF, Siniscalchi A, Sullivan AD, West N, Johansson MA, Biggerstaff M. Applying infectious disease forecasting to public health: a path forward using influenza forecasting examples. BMC Public Health 2019;19(1)
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Baltrusaitis K, Vespignani A, Rosenfeld R, Gray J, Raymond D, Santillana M. Differences in Regional Patterns of Influenza Activity Across Surveillance Systems in the United States: Comparative Evaluation. JMIR Public Health and Surveillance 2019;5(4):e13403
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Mavragani A, Ochoa G. Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health and Surveillance 2019;5(2):e13439
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Talaei-Khoei A, Wilson JM, Kazemi S. Period of Measurement in Time-Series Predictions of Disease Counts from 2007 to 2017 in Northern Nevada: Analytics Experiment. JMIR Public Health and Surveillance 2019;5(1):e11357
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Darwish A, Rahhal Y, Jafar A. A comparative study on predicting influenza outbreaks using different feature spaces: application of influenza-like illness data from Early Warning Alert and Response System in Syria. BMC Research Notes 2020;13(1)
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Roth JA, Battegay M, Juchler F, Vogt JE, Widmer AF. Introduction to Machine Learning in Digital Healthcare Epidemiology. Infection Control & Hospital Epidemiology 2018;39(12):1457
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Jarynowski A, Wójta-Kempa M, BElik V. Perception of Emergent Epidemic of COVID-2019 / SARS CoV-2 on the Polish Internet. SSRN Electronic Journal 2020;
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Bowen DA, Wang J, Holland K, Bartholow B, Sumner SA. Conversational topics of social media messages associated with state-level mental distress rates. Journal of Mental Health 2020;29(2):234
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Clemente L, Lu F, Santillana M. Improved Real-Time Influenza Surveillance: Using Internet Search Data in Eight Latin American Countries. JMIR Public Health and Surveillance 2019;5(2):e12214
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Soliman M, Lyubchich V, Gel YR. Complementing the power of deep learning with statistical model fusion: Probabilistic forecasting of influenza in Dallas County, Texas, USA. Epidemics 2019;28:100345
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Tideman S, Santillana M, Bickel J, Reis B. Internet search query data improve forecasts of daily emergency department volume. Journal of the American Medical Informatics Association 2019;26(12):1574
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. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206
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Rangarajan P, Mody SK, Marathe M, Priedhorsky R. Forecasting dengue and influenza incidences using a sparse representation of Google trends, electronic health records, and time series data. PLOS Computational Biology 2019;15(11):e1007518
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Kolff CA, Scott VP, Stockwell MS. The use of technology to promote vaccination: A social ecological model based framework. Human Vaccines & Immunotherapeutics 2018;14(7):1636
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Su K, Xu L, Li G, Ruan X, Li X, Deng P, Li X, Li Q, Chen X, Xiong Y, Lu S, Qi L, Shen C, Tang W, Rong R, Hong B, Ning Y, Long D, Xu J, Shi X, Yang Z, Zhang Q, Zhuang Z, Zhang L, Xiao J, Li Y. Forecasting influenza activity using self-adaptive AI model and multi-source data in Chongqing, China. EBioMedicine 2019;47:284
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Tseng VS, Ying JJ, Wong ST, Cook DJ, Liu J. Computational Intelligence Techniques for Combating COVID-19: A Survey. IEEE Computational Intelligence Magazine 2020;15(4):10
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Masthi R, Jahan A, Bharathi D, Abhilash P, Kaniyarakkal V, TV S, Gowda G, TS R, Goud R, Rao S, Hegde A. Postcode based participatory disease surveillance systems : a comparison with traditional risk-based surveillance and its application in the COVID-19 pandemic (Preprint). JMIR Public Health and Surveillance 2020;
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. Public interest in spa therapy during the COVID-19 pandemic: analysis of Google Trends data among Turkey. International Journal of Biometeorology 2021;65(6):945
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Vigfusson Y, Karlsson TA, Onken D, Song C, Einarsson AF, Kishore N, Mitchell RM, Brooks-Pollock E, Sigmundsdottir G, Danon L. Cell-phone traces reveal infection-associated behavioral change. Proceedings of the National Academy of Sciences 2021;118(6)
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Kardeş S, Kuzu AS, Raiker R, Pakhchanian H, Karagülle M. Public interest in rheumatic diseases and rheumatologist in the United States during the COVID-19 pandemic: evidence from Google Trends. Rheumatology International 2021;41(2):329
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Runkle JD, Sugg MM, Graham G, Hodge B, March T, Mullendore J, Tove F, Salyers M, Valeika S, Vaughan E. Participatory COVID-19 Surveillance Tool in Rural Appalachia. Public Health Reports 2021;136(3):327
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Nsoesie EO, Oladeji O, Abah ASA, Ndeffo-Mbah ML. Forecasting influenza-like illness trends in Cameroon using Google Search Data. Scientific Reports 2021;11(1)
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Kogan NE, Clemente L, Liautaud P, Kaashoek J, Link NB, Nguyen AT, Lu FS, Huybers P, Resch B, Havas C, Petutschnig A, Davis J, Chinazzi M, Mustafa B, Hanage WP, Vespignani A, Santillana M. An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time. Science Advances 2021;7(10)
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Agarwal A, Uniyal D, Toshniwal D, Deb D. Dense Vector Embedding Based Approach to Identify Prominent Disseminators From Twitter Data Amid COVID-19 Outbreak. IEEE Transactions on Emerging Topics in Computational Intelligence 2021;5(3):308
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Miliou I, Xiong X, Rinzivillo S, Zhang Q, Rossetti G, Giannotti F, Pedreschi D, Vespignani A, Viboud C. Predicting seasonal influenza using supermarket retail records. PLOS Computational Biology 2021;17(7):e1009087
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Turtle J, Riley P, Ben-Nun M, Riley S, Perkins A. Accurate influenza forecasts using type-specific incidence data for small geographic units. PLOS Computational Biology 2021;17(7):e1009230
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Kiang MV, Chen JT, Krieger N, Buckee CO, Alexander MJ, Baker JT, Buckner RL, Coombs G, Rich-Edwards JW, Carlson KW, Onnela J. Sociodemographic characteristics of missing data in digital phenotyping. Scientific Reports 2021;11(1)
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Oto OA, Kardeş S, Guller N, Safak S, Dirim AB, Başhan Y, Demir E, Artan AS, Yazıcı H, Turkmen A. Impact of the COVID-19 pandemic on interest in renal diseases. Environmental Science and Pollution Research 2022;29(1):711
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Jing F, Li Z, Qiao S, Zhang J, Olatosi B, Li X. Using geospatial social media data for infectious disease studies: a systematic review. International Journal of Digital Earth 2023;16(1):130
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ERDEM SULTANOĞLU T, ATAOĞLU S. COVID-19 Pandemisi Döneminde Romatizmal Hastalıklara Halkın İlgisi: Google Trends Verilerinin Analizi. Sağlık Bilimlerinde Değer 2022;12(1):147
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Wang H, Qian G. Guideline-Driven Medical Decision Support Methods for Family Healthcare. IEEE Access 2021;9:116612
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Olukanmi SO, Nelwamondo FV, Nwulu NI. Utilizing Google Search Data With Deep Learning, Machine Learning and Time Series Modeling to Forecast Influenza-Like Illnesses in South Africa. IEEE Access 2021;9:126822
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Amusa LB, Twinomurinzi H, Okonkwo CW. Modeling COVID-19 incidence with Google Trends. Frontiers in Research Metrics and Analytics 2022;7
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Santangelo OE, Gentile V, Pizzo S, Giordano D, Cedrone F. Machine Learning and Prediction of Infectious Diseases: A Systematic Review. Machine Learning and Knowledge Extraction 2023;5(1):175
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. Social Networks of Lexical Innovation. Investigating the Social Dynamics of Diffusion of Neologisms on Twitter. Frontiers in Artificial Intelligence 2021;4
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Jusup M, Holme P, Kanazawa K, Takayasu M, Romić I, Wang Z, Geček S, Lipić T, Podobnik B, Wang L, Luo W, Klanjšček T, Fan J, Boccaletti S, Perc M. Social physics. Physics Reports 2022;948:1
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Khakimova A, Abdollahi L, Zolotarev O, Rahim F. Global interest in vaccines during the COVID‐19 pandemic: Evidence from Google Trends. Vaccine: X 2022;10:100152
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Amusa LB, Twinomurinzi H, Phalane E, Phaswana-Mafuya RN. Big Data and Infectious Disease Epidemiology: Bibliometric Analysis and Research Agenda. Interactive Journal of Medical Research 2023;12:e42292
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Wolfe MK, Duong D, Bakker KM, Ammerman M, Mortenson L, Hughes B, Arts P, Lauring AS, Fitzsimmons WJ, Bendall E, Hwang CE, Martin ET, White BJ, Boehm AB, Wigginton KR. Wastewater-Based Detection of Two Influenza Outbreaks. Environmental Science & Technology Letters 2022;9(8):687
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Trevino J, Malik S, Schmidt M. Integrating Google Trends Search Engine Query Data Into Adult Emergency Department Volume Forecasting: Infodemiology Study. JMIR Infodemiology 2022;2(1):e32386
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Stolerman LM, Clemente L, Poirier C, Parag KV, Majumder A, Masyn S, Resch B, Santillana M. Using digital traces to build prospective and real-time county-level early warning systems to anticipate COVID-19 outbreaks in the United States. Science Advances 2023;9(3)
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Athanasiou M, Fragkozidis G, Zarkogianni K, Nikita KS. Long Short-term Memory–Based Prediction of the Spread of Influenza-Like Illness Leveraging Surveillance, Weather, and Twitter Data: Model Development and Validation. Journal of Medical Internet Research 2023;25:e42519
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Sumner SA, Bowen D, Holland K, Zwald ML, Vivolo-Kantor A, Guy GP, Heuett WJ, Pressley DP, Jones CM. Estimating Weekly National Opioid Overdose Deaths in Near Real Time Using Multiple Proxy Data Sources. JAMA Network Open 2022;5(7):e2223033
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Zhang D, Ge Y, Wu X, Liu H, Zhang W, Lai S. Data-Driven Models Informed by Spatiotemporal Mobility Patterns for Understanding Infectious Disease Dynamics. ISPRS International Journal of Geo-Information 2023;12(7):266
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Luca M, Campedelli GM, Centellegher S, Tizzoni M, Lepri B. Crime, inequality and public health: a survey of emerging trends in urban data science. Frontiers in Big Data 2023;6
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Wang D, Lentzen M, Botz J, Valderrama D, Deplante L, Perrio J, Génin M, Thommes E, Coudeville L, Fröhlich H. Development of an early alert model for pandemic situations in Germany. Scientific Reports 2023;13(1)
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According to Crossref, the following books are citing this article (DOI 10.2196/publichealth.8950):
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Simsek M, Obinikpo AA, Kantarci B. Connected Health in Smart Cities. 2020. Chapter 3:23
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Samaras L, García-Barriocanal E, Sicilia M. Innovation in Health Informatics. 2020. :39
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Ajagbe SA, Awotunde JB, Oladipupo MA, Oye OE. Machine Learning for Critical Internet of Medical Things. 2022. Chapter 2:31
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Awotunde JB, Oluwabukonla S, Chakraborty C, Bhoi AK, Ajamu GJ. Decision Sciences for COVID-19. 2022. Chapter 1:3
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Gilbert J, Niu J, de Montigny S, Ng V, Rees E. AI for Disease Surveillance and Pandemic Intelligence. 2022. Chapter 9:101
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Awotunde JB, Jimoh RG, Matiluko OE, Gbadamosi B, Ajamu GJ. Intelligent Interactive Multimedia Systems for e-Healthcare Applications. 2022. Chapter 11:191
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