Fecha de publicación: -- Fuente: PubMed "rice" Geohealth. 2026 Nov 6;10(11):e2026GH002245. doi: 10.1029/2026GH002245. eCollection 2026 Nov.ABSTRACTOpisthorchis viverrini (OV) infection is a major public health problem in northeastern Thailand, where it causes cholangiocarcinoma. This study developed a spatial prediction model for OV infection risk in Sakon Nakhon Province by integrating Sentinel-2, Sentinel-3, and CHIRPS data with hexagonal grid optimization and ensemble machine learning. Eleven environmental indices were aggregated using a newly developed Hd index, which identified an optimal hexagonal grid size of d = 2,100 m. Three models-Random Forest, XGBoost, and Multi-scale Geographically Weighted Regression (MGWR)-were compared using R 2, root mean square error, ROC-AUC at exceedance thresholds (1%, 2%, 3%), 5-fold cross-validation, and SHAP analysis. XGBoost outperformed the other models, achieving the highest test-set R 2 (0.894) and AUC (0.955) at the >3% threshold, with stable cross-validated performance (mean R 2 = 0.83). SHAP analysis identified GVMI as the most influential predictor (mean |SHAP| = 0.125), followed by MNDWI (0.090) and SPI-6 (0.070). Moderate moisture conditions and above-average rainfall increased risk, whereas deep open water reduced it. The resulting risk map supports targeted surveillance and resource allocation for OV control in endemic areas.PMID:42999529 | PMC:PMC13743928 | DOI:10.1029/2026GH002245