Published on in Vol 3, No 4 (2017): Oct-Dec

Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches

Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches

Combining Participatory Influenza Surveillance with Modeling and Forecasting: Three Alternative Approaches

Journals

  1. Singh M, Sarkhel P, Kang G, Marathe A, Boyle K, Murray-Tuite P, Abbas K, 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) View
  2. Luo H, Lie Y, Prinzen F. 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 View
  3. Chan A, Brownstein J. Putting the Public Back in Public Health — Surveying Symptoms of Covid-19. New England Journal of Medicine 2020;383(7) View
  4. Scarpino S, Scott J, Eggo R, Clements B, Dimitrov N, Meyers L, Ferrari M. Socioeconomic bias in influenza surveillance. PLOS Computational Biology 2020;16(7):e1007941 View
  5. Weitzman E, Magane K, Chen P, Amiri H, Naimi T, Wisk L. Online Searching and Social Media to Detect Alcohol Use Risk at Population Scale. American Journal of Preventive Medicine 2020;58(1):79 View
  6. Caldwell W, Fairchild G, Del Valle S. 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 View
  7. Chen J, Marathe A, Marathe M. Feedback Between Behavioral Adaptations and Disease Dynamics. Scientific Reports 2018;8(1) View
  8. 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 View
  9. 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 View
  10. 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 View
  11. Koehlmoos T, Janvrin M, 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 View
  12. Lapointe-Shaw L, Rader B, Astley C, Hawkins J, Bhatia D, Schatten W, Lee T, Liu J, Ivers N, Stall N, Gournis E, Tuite A, Fisman D, Bogoch I, Brownstein J, Di Gennaro F. Web and phone-based COVID-19 syndromic surveillance in Canada: A cross-sectional study. PLOS ONE 2020;15(10):e0239886 View
  13. Lu F, Nguyen A, Link N, Molina M, Davis J, 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 View
  14. Tozzi A, 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 View
  15. Gardner R, Haskell J, Jenkins B, Capizzo L, Cooper E, 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 View
  16. 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) View
  17. Maharaj A, Parker J, Hopkins J, Gournis E, Bogoch I, Rader B, Astley C, Ivers N, Hawkins J, Lee L, Tuite A, Fisman D, Brownstein J, Lapointe-Shaw L, Lau E. Comparison of longitudinal trends in self-reported symptoms and COVID-19 case activity in Ontario, Canada. PLOS ONE 2022;17(1):e0262447 View
  18. Ganser I, Thiébaut R, Buckeridge D. Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis. JMIR Public Health and Surveillance 2022;8(10):e36211 View
  19. 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 View
  20. 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 View
  21. Luca M, Campedelli G, 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 View
  22. Morris M, Hayes P, Cox I, 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 View
  23. Leal Neto O, Paolotti D, Dalton C, Carlson S, Susumpow P, Parker M, Phetra P, Lau E, Colizza V, Jan van Hoek A, Kjelsø C, Brownstein J, Smolinski M. Enabling Multicentric Participatory Disease Surveillance for Global Health Enhancement: Viewpoint on Global Flu View. JMIR Public Health and Surveillance 2023;9:e46644 View
  24. 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) View
  25. Akiya I, Ishihara T, Yamamoto K. A Comparison of Synthetic Data Generation Techniques for Control Group Survival Data in Oncology Clinical Trials: Simulation Study (Preprint). JMIR Medical Informatics 2023 View
  26. Papagiannopoulou E, Bossa M, Deligiannis N, Sahli H. Long-Term Regional Influenza-Like-Illness Forecasting Using Exogenous Data. IEEE Journal of Biomedical and Health Informatics 2024;28(6):3781 View

Books/Policy Documents

  1. Spitzberg B, Tsou M, Jung C. The Handbook of Applied Communication Research. View