Speaker
Description
West Nile Virus (WNV) is a mosquito-borne zoonotic disease increasingly recognized as a public health concern across Europe, particularly due to its complex transmission dynamics influenced by climatic, environmental, and anthropogenic factors. In this study, we implemented a predictive machine learning framework integrating Random Forest classifiers and explainable artificial intelligence (XAI) techniques, specifically SHapley Additive exPlanations (SHAP), to identify critical environmental drivers of WNV outbreaks across Italian provinces from 2012 to 2024. The model incorporated diverse environmental datasets, including climatic variables, land-use patterns, pesticide residues in surface waters, and epidemiological surveillance data. Our results demonstrated robust predictive performance, especially in temporal cross-validation (accuracy = 0.71 ± 0.01), highlighting the model's effectiveness in forecasting disease incidence. Crucially, SHAP analysis underscored the significant influence of pesticide contamination in surface waters, alongside landscape features such as grasslands and topographic flatness, in determining WNV risk. These findings emphasize the pivotal role of agricultural pesticide runoff, suggesting ecological disruptions that may facilitate mosquito proliferation and virus transmission. This research reinforces the necessity of incorporating environmental pollution data within a One Health framework to enhance early-warning systems and inform targeted public health interventions.
