Published on in Vol 23, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/28876, first published .
Forecasting the COVID-19 Epidemic by Integrating Symptom Search Behavior Into Predictive Models: Infoveillance Study

Forecasting the COVID-19 Epidemic by Integrating Symptom Search Behavior Into Predictive Models: Infoveillance Study

Forecasting the COVID-19 Epidemic by Integrating Symptom Search Behavior Into Predictive Models: Infoveillance Study

Journals

  1. Husnayain A, Shim E, Fuad A, Su E. Predicting New Daily COVID-19 Cases and Deaths Using Search Engine Query Data in South Korea From 2020 to 2021: Infodemiology Study. Journal of Medical Internet Research 2021;23(12):e34178 View
  2. Braun D, Ingram D, Ingram D, Khan B, Marsh J, McAndrew T. Crowdsourced Perceptions of Human Behavior to Improve Computational Forecasts of US National Incident Cases of COVID-19: Survey Study. JMIR Public Health and Surveillance 2022;8(12):e39336 View
  3. 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 View
  4. Ma S, Sun Y, Yang S. Using Internet Search Data to Forecast COVID-19 Trends: A Systematic Review. Analytics 2022;1(2):210 View
  5. Wang X, Dong Y, Thompson W, Nair H, Li Y. Short-term local predictions of COVID-19 in the United Kingdom using dynamic supervised machine learning algorithms. Communications Medicine 2022;2(1) View
  6. Saegner T, Austys D. Forecasting and Surveillance of COVID-19 Spread Using Google Trends: Literature Review. International Journal of Environmental Research and Public Health 2022;19(19):12394 View
  7. Deiner M, Kaur G, McLeod S, Schallhorn J, Chodosh J, Hwang D, Lietman T, Porco T. A Google Trends Approach to Identify Distinct Diurnal and Day-of-Week Web-Based Search Patterns Related to Conjunctivitis and Other Common Eye Conditions: Infodemiology Study. Journal of Medical Internet Research 2022;24(7):e27310 View
  8. Turvy A. State-Level COVID-19 Symptom Searches and Case Data: Quantitative Analysis of Political Affiliation as a Predictor for Lag Time Using Google Trends and Centers for Disease Control and Prevention Data. JMIR Formative Research 2022;6(12):e40825 View
  9. Pellegrini M, Ferrucci E, Guaraldi F, Bernabei F, Scorcia V, Giannaccare G. Emerging application of Google Trends searches on “conjunctivitis” for tracing the course of COVID-19 pandemic. European Journal of Ophthalmology 2022;32(4):1947 View
  10. Lyu H, Imtiaz A, Zhao Y, Luo J. Human behavior in the time of COVID-19: Learning from big data. Frontiers in Big Data 2023;6 View
  11. Zayed B, Talaia A, Gaaboobah M, Amer S, Mansour F. Google Trends as a predictive tool in the era of COVID-19: a scoping review. Postgraduate Medical Journal 2023;99(1175):962 View
  12. Morokhovets H, Kaidashev I. A MATHEMATICAL MODEL FOR PROGNOSIS OF THE COVID-19 INCIDENCE IN UKRAINE USING GOOGLE TRENDS RESOURCES IN REAL-TIME AND FOR THE FUTURE PERIOD. The Medical and Ecological Problems 2022;26(3-4):3 View
  13. Clark E, Neumann S, Hopkins S, Kostopoulos A, Hagerman L, Dobbins M. Changes to Public Health Surveillance Methods Due to the COVID-19 Pandemic: Scoping Review. JMIR Public Health and Surveillance 2024;10:e49185 View
  14. Lyu S, Adegboye O, Adhinugraha K, Emeto T, Taniar D. Analysing the impact of comorbid conditions and media coverage on online symptom search data: a novel AI-based approach for COVID-19 tracking. Infectious Diseases 2024;56(5):348 View

Books/Policy Documents

  1. Butt Z. Accelerating Strategic Changes for Digital Transformation in the Healthcare Industry. View