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):e45 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

Books/Policy Documents

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