Work

Spatial Prediction of Opisthorchis viverrini Infection Risk Using Multi‐Source Remote Sensing, Hexagonal Grid Optimization, and Ensemble Machine Learning: A Case Study From Northeastern Thailand

Year: 2026

Type: article

Abstract: Opisthorchis 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.

Source: GeoHealth

DOI: 10.1029/2026gh002245

Authors: Patiwat Littidej, Benjamabhorn Pumhirunroj, Thidarut Boonmars, Atchara Artchayasawat, Nutchanat Buasri, Donald Slack