Published on 01.11.17 in Vol 3, No 4 (2017): Oct-Dec
Works citing "Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches"
According to Crossref, the following articles are citing this article (DOI 10.2196/publichealth.7344):
(note that this is only a small subset of citations)
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Singh M, Sarkhel P, Kang GJ, Marathe A, Boyle K, Murray-Tuite P, Abbas KM, Swarup S. Impact of demographic disparities in social distancing and vaccination on influenza epidemics in urban and rural regions of the United States. BMC Infectious Diseases 2019;19(1)
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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 and Surveillance 2020;6(2):e18576
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Chan AT, Brownstein JS. Putting the Public Back in Public Health — Surveying Symptoms of Covid-19. New England Journal of Medicine 2020;383(7):e45
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Scarpino SV, Scott JG, Eggo RM, Clements B, Dimitrov NB, Meyers LA, Ferrari M. Socioeconomic bias in influenza surveillance. PLOS Computational Biology 2020;16(7):e1007941
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Weitzman ER, Magane KM, Chen P, Amiri H, Naimi TS, Wisk LE. Online Searching and Social Media to Detect Alcohol Use Risk at Population Scale. American Journal of Preventive Medicine 2020;58(1):79
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Caldwell WK, Fairchild G, Del Valle SY. Surveilling Influenza Incidence With Centers for Disease Control and Prevention Web Traffic Data: Demonstration Using a Novel Dataset. Journal of Medical Internet Research 2020;22(7):e14337
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Chen J, Marathe A, Marathe M. Feedback Between Behavioral Adaptations and Disease Dynamics. Scientific Reports 2018;8(1)
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Wakamiya S, Matsune S, Okubo K, Aramaki E. Causal Relationships Among Pollen Counts, Tweet Numbers, and Patient Numbers for Seasonal Allergic Rhinitis Surveillance: Retrospective Analysis. Journal of Medical Internet Research 2019;21(2):e10450
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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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Fujibayashi K, Takahashi H, Tanei M, Uehara Y, Yokokawa H, Naito T. A New Influenza-Tracking Smartphone App (Flu-Report) Based on a Self-Administered Questionnaire: Cross-Sectional Study. JMIR mHealth and uHealth 2018;6(6):e136
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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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Lapointe-Shaw L, Rader B, Astley CM, Hawkins JB, Bhatia D, Schatten WJ, Lee TC, Liu JJ, Ivers NM, Stall NM, Gournis E, Tuite AR, Fisman DN, Bogoch II, Brownstein JS, Di Gennaro F. Web and phone-based COVID-19 syndromic surveillance in Canada: A cross-sectional study. PLOS ONE 2020;15(10):e0239886
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Lu FS, Nguyen AT, Link NB, Molina M, Davis JT, Chinazzi M, Xiong X, Vespignani A, Lipsitch M, Santillana M, Viboud C. Estimating the cumulative incidence of COVID-19 in the United States using influenza surveillance, virologic testing, and mortality data: Four complementary approaches. PLOS Computational Biology 2021;17(6):e1008994
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Tozzi AE, Gesualdo F, Urbani E, Sbenaglia A, Ascione R, Procopio N, Croci I, Rizzo C. Digital Surveillance Through an Online Decision Support Tool for COVID-19 Over One Year of the Pandemic in Italy: Observational Study. Journal of Medical Internet Research 2021;23(8):e29556
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Gardner RL, Haskell J, Jenkins B, Capizzo LF, Cooper EL, Morphis B. Innovative Use of a Mobile Web Application to Remotely Monitor Nonhospitalized Patients with COVID-19. Telemedicine and e-Health 2022;28(9):1285
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Marmara V, Marmara D, McMenemy P, Kleczkowski A. Cross-sectional telephone surveys as a tool to study epidemiological factors and monitor seasonal influenza activity in Malta. BMC Public Health 2021;21(1)
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Maharaj AS, Parker J, Hopkins JP, Gournis E, Bogoch II, Rader B, Astley CM, Ivers NM, Hawkins JB, Lee L, Tuite AR, Fisman DN, Brownstein JS, Lapointe-Shaw L, Lau EH. Comparison of longitudinal trends in self-reported symptoms and COVID-19 case activity in Ontario, Canada. PLOS ONE 2022;17(1):e0262447
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Ganser I, Thiébaut R, Buckeridge DL. Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis. JMIR Public Health and Surveillance 2022;8(10):e36211
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Leal-Neto O, Egger T, Schlegel M, Flury D, Sumer J, Albrich W, Babouee Flury B, Kuster S, Vernazza P, Kahlert C, Kohler P. Digital SARS-CoV-2 Detection Among Hospital Employees: Participatory Surveillance Study. JMIR Public Health and Surveillance 2021;7(11):e33576
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Bedi J, Vijay D, Dhaka P, Singh Gill J, Barbuddhe S. Emergency preparedness for public health threats, surveillance, modelling & forecasting. Indian Journal of Medical Research 2021;153(3):287
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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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Morris M, Hayes P, Cox IJ, Lampos V, Lofgren E. Neural network models for influenza forecasting with associated uncertainty using Web search activity trends. PLOS Computational Biology 2023;19(8):e1011392
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Leal Neto O, Paolotti D, Dalton C, Carlson S, Susumpow P, Parker M, Phetra P, Lau EHY, Colizza V, Jan van Hoek A, Kjelsø C, Brownstein JS, Smolinski MS. Enabling Multicentric Participatory Disease Surveillance for Global Health Enhancement: Viewpoint on Global Flu View. JMIR Public Health and Surveillance 2023;9:e46644
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Bokányi E, Vizi Z, Koltai J, Röst G, Karsai M. Real-time estimation of the effective reproduction number of COVID-19 from behavioral data. Scientific Reports 2023;13(1)
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According to Crossref, the following books are citing this article (DOI 10.2196/publichealth.7344):
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Spitzberg BH, Tsou M, Jung C. The Handbook of Applied Communication Research. 2020. :163
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