This paper is in the following e-collection/theme issue:

JMIR Theme Issue: COVID-19 Special Issue (2491) Infodemiology and Infoveillance (1445) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1085) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1530) Outbreak and Pandemic Preparedness and Management (1779) Infectious Diseases (non-STD/STI) (1654) Statistical Methods for Surveillance and Population Health (170) Epidemiology (173) Innovative Methods in Public Health and Surveillance (357) Infoveillance and Social Listening (241)

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

Authors of this article:

Atina Husnayain1 Author Orcid Image ;   Eunha Shim2 Author Orcid Image ;   Anis Fuad3 Author Orcid Image ;   Emily Chia-Yu Su1, 4 Author Orcid Image
Article Authors Cited by (9) Tweetations (3) Metrics

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
  7. Xu D, Chan W, Haron H. Enhancing infectious disease prediction model selection with multi-objective optimization: an empirical study. PeerJ Computer Science 2024;10:e2217 View
  8. Ahn S, Yim K, Won H, Kim K, Jeong D. Discovering Time-Varying Public Interest for COVID-19 Case Prediction in South Korea Using Search Engine Queries: Infodemiology Study. Journal of Medical Internet Research 2024;26:e63476 View
  9. Li H, Zhang N, Ma X, Wang Y, Yang F, Wang W, Huang Y, Xie Y, Du Y. Google Trends applications for COVID-19 pandemic: A bibliometric analysis. DIGITAL HEALTH 2025;11 View