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

Infodemiology and Infoveillance (2037) Information Retrieval (497) mHealth for Data Collection and Research (1775) Theoretical Frameworks and Concepts (580) Machine Learning (3318) Outbreak and Pandemic Preparedness and Management (1975) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (175) AI Applications in Public Health (114)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/63476, first published .
Laptop and smartphone displaying Google Trends data for "covid" and news articles.

Discovering Time-Varying Public Interest for COVID-19 Case Prediction in South Korea Using Search Engine Queries: Infodemiology Study

Discovering Time-Varying Public Interest for COVID-19 Case Prediction in South Korea Using Search Engine Queries: Infodemiology Study

Authors of this article:

Seong-Ho Ahn1 Author Orcid Image ;   Kwangil Yim2 Author Orcid Image ;   Hyun-Sik Won1 Author Orcid Image ;   Kang-Min Kim1, 3 Author Orcid Image ;   Dong-Hwa Jeong1, 4 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics

Journals

  1. Mehmood I, Zahra Z, Hussain I. Exploring clinical pathways: a novel BERTopic framework for dynamic analysis using temporal binning and semantic drift. The Journal of Supercomputing 2026;82(4) View
  2. Kim M, Jang B. Cell-Attentive LSTM With Lag-Aware Feature Selection for Multi-Week Epidemic Forecasting. IEEE Access 2026;14:151311 View