Spatial prediction of the probability of liver fluke infection using a geographic weighted regression (GWR) model in waterways connecting the Mekong River, Sakon Nakhon of Thailand

The results of the GWR comparison test were derived from both infected and hexagonal water source data. Models constructed from hexagonal grids consistently outperformed all alternative models, with R² values improving to 58.7 %, 41.1 %, and 53.2 % for Model-1, Model-2, and Model-3, respectively. The RMSE also showed significant improvement, decreasing to 77.1 %, 60.2 %, and 67.2 %. Additionally, the model's accuracy was evaluated using AUC values of 0.725, 0.652, and 0.707, indicating that the developed model can effectively predict water source infections. Model-1 emerged as the most representative across all tests, incorporating soil drainage factors and road proximity as key influences on water source infection. Finally, the results are presented as infection prediction maps for each grid, highlighting areas of both overestimation and underestimation. The most accurate prediction model identified that over 95 % of grids had a high degree of accuracy. This study is anticipated to be applicable to infections caused by other water-mediated parasites.