Published on in Vol 23, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34178, first published .
Predicting New Daily COVID-19 Cases and Deaths Using Search Engine Query Data in South Korea From 2020 to 2021: Infodemiology Study

Predicting New Daily COVID-19 Cases and Deaths Using Search Engine Query Data in South Korea From 2020 to 2021: Infodemiology Study

Predicting New Daily COVID-19 Cases and Deaths Using Search Engine Query Data in South Korea From 2020 to 2021: Infodemiology Study

Journals

  1. Strzelecki A. The Apple Mobility Trends Data in Human Mobility Patterns during Restrictions and Prediction of COVID-19: A Systematic Review and Meta-Analysis. Healthcare 2022;10(12):2425 View
  2. Ravkin H, Yom-Tov E, Nesher L. The Effect of Nonpharmaceutical Interventions Implemented in Response to the COVID-19 Pandemic on Seasonal Respiratory Syncytial Virus: Analysis of Google Trends Data. Journal of Medical Internet Research 2022;24(12):e42781 View
  3. Niu Q, Liu J, Zhao Z, Onishi M, Kawaguchi A, Bandara A, Harada K, Aoyama T, Nagai-Tanima M. Explanation of hand, foot, and mouth disease cases in Japan using Google Trends before and during the COVID-19: infodemiology study. BMC Infectious Diseases 2022;22(1) View
  4. Cha E, Jeon H, Landenmark H. The effect of COVID-19 pandemic on sleep-related problems in adults and elderly citizens: An infodemiology study using relative search volume data. PLOS ONE 2022;17(7):e0271059 View
  5. Athanasiou M, Fragkozidis G, Zarkogianni K, Nikita K. 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 View
  6. Kaur M, Cargill T, Hui K, Vu M, Bragazzi N, Kong J. A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets. JMIR Formative Research 2024;8:e46087 View