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

GIS (Geographic Information Systems) Applications in Public Health and Spatial Epidemiology (229) Public (e)Health, Digital Epidemiology and Public Health Informatics (2290) Statistical Methods for Surveillance and Population Health (250) Infectious Diseases (non-STD/STI) (1926) Use Cases in Human, Animal, and Zoonotic Communicable/Infectious Disease Surveillance (155)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68437, first published .
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Distribution and Risk Factors of Scrub Typhus in South Korea, From 2013 to 2019: Bayesian Spatiotemporal Analysis

Distribution and Risk Factors of Scrub Typhus in South Korea, From 2013 to 2019: Bayesian Spatiotemporal Analysis

Authors of this article:

Jeehyun Kim1 Author Orcid Image ;   Penelope Vounatsou2 Author Orcid Image ;   Byung Chul Chun1 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Kim W. Integrating Environmental and Seasonal Factors in a Machine Learning Model for Predicting Scrub Typhus Incidence. Journal of Environmental Health Sciences 2025;51(6):452 View
  2. Nyengere J, Mbewe W, Malalu L, Tholo H, Njala A, Sembo T, Kumpolota S, Mvula R, Chisenga C, Kanyika-Mbewe C, Maluwa A, Eregno F. Geospatial modelling for zoonotic disease hotspot identification within a One Health framework: a systematic review. One Health Outlook 2026;8(1) View
  3. Kim Y, Kwon D, Hasahya E, Lee H. Spatiotemporal prediction of scrub typhus incidence and environmental risk factors in Republic of Korea: a Bayesian hierarchical approach. Infectious Diseases of Poverty 2026;15(1) View