Modelling the decadal expansion of West Nile virus in Italy: the role of climatic, anthropogenic, and macroecological drivers

The research focuses on modelling West Nile virus (WNV) infection risk in Italy using machine learning based on human case data from 2014 to 2024. The model integrated environmental, socio-economic, and macroecological factors to predict risk at the municipal level. Results showed an elevenfold increase in affected municipalities from 184 in 2014 to 2,012 in 2024. The most important risk factor was minimum seasonal temperature, followed by monitoring year and population density. The model successfully identified emerging risk hotspots along the Adriatic coast and in southern Italy before the official occurrence of human cases in 2025. The research demonstrates that incorporating macroecological drivers into WNV risk modelling enables better understanding and prediction of virus expansion.