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

Infodemiology and Infoveillance (1598) Outbreak and Pandemic Preparedness and Management (1848) mHealth for Data Collection and Research (1261) Machine Learning (2229) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (104) Information Retrieval (363) Theoretical Frameworks and Concepts (374) AI Applications in Public Health (37)

Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/63476, first published .
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

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
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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