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

GIS (Geographic Information Systems) Applications in Public Health and Spatial Epidemiology (144) Public (e)Health, Digital Epidemiology and Public Health Informatics (1055) Infectious Diseases (non-STD/STI) (1738) Use Cases in Human, Animal, and Zoonotic Communicable/Infectious Disease Surveillance (146) Statistical Methods for Surveillance and Population Health (193)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/68437, first published .
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

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 (2) 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