Unraveling the drivers of leptospirosis risk in Thailand using machine learning.

ABSTRACT: Leptospirosis poses a significant public health challenge in Thailand, driven by a complex mix of environmental and socioeconomic factors. This study develops an XGBoost machine learning model to predict leptospirosis outbreak risk at the provincial level in Thailand, integrating climatic, socioeconomic, and agricultural features. Using national surveillance data from 2007-2022, the model was trained to classify provinces as high or low risk based on the median incidence rate. The model's predictive performance was validated for the years 2018-2022, spanning pre-COVID-19, COVID-19, and post-COVID-19 periods. SHapley Additive exPlanation (SHAP) analysis was employed to identify key predictive factors. The optimized XGBoost model achieved high predictive accuracy for the pre-pandemic (AUC = 

SUBMITTER: Suttirat P 

PROVIDER: S-EPMC12539691 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature