Ecological epidemiology insights into clonorchiosis endemicity in Guangxi, China and Vietnam: a comprehensive machine learning analysis.

Background: Clonorchis sinensis, the liver fluke responsible for clonorchiosis, presents a persistent public health burden in Guangxi (Southern China) and Vietnam. Its transmission is influenced by a complex interplay of ecological, climatic, and socio-cultural factors.

Methods: We compiled infection occurrence data from systematic literature reviews and national surveys conducted between 2000 and 2018. Environmental and climatic predictors were obtained from long-term raster datasets. Machine learning models, including logistic regression and tree-based ensemble methods, were used to assess associations between predictor variables and C. sinensis presence. Partial dependence plots were employed to refine predictor selection and explore marginal effects.

Results: Raw freshwater fish consumption was identified as the most influential predictor. In Guangxi, 54.9% of counties reported raw fish consumption, compared to 31.7% in Vietnam. Logistic regression achieved the highest predictive accuracy (AUC = 0.941). Climatic comparisons showed that Vietnam had a higher annual mean temperature (Bio1: 23.37 °C vs. 20.86 °C), greater temperature seasonality (Bio4: 609.33 vs. 464.92), and higher annual precipitation (Bio12: 1731.64 mm vs. 1607.56 mm) than Guangxi, contributing to spatial differences in endemicity. High-risk zones were concentrated along the China-Vietnam border, suggesting the need for geographically targeted interventions.

Conclusion: The findings underscore the combined influence of ecological and behavioral factors on C. sinensis transmission. The predictive modeling framework offers valuable insights for surveillance planning and cross-border disease control, reinforcing the role of ecological epidemiology in guiding parasitic disease prevention strategies.