Geospatial insights and artificial intelligence in West Nile virus disease Epidemiology: The Italy case study

Objectives: Although artificial intelligence, including machine learning offers transformative potential, its use in infectious disease epidemiology has been understudied. Emerging infectious diseases including WNV infection remains a leading cause of mortality globally. The study aimed to provide a decade-long insight into the global trends on WNV outbreaks. Study design: The study offers a ML-powered framework to guide preventive strategies. Methods: Environmental indices including neighbourhood greenness, forest cover, and temperature were captured using 250m resolution MODIS satellite imagery. Seasonal human, avian and equine WNV surveillance records and other regional indicators for Italy from 2013 to 2023 were retrieved from surveillance repositories. Spatial trends and interactions were evaluated using spatial autocorrelation analyses. Further, a ML architecture was developed for prediction of high or low WNV classification based on satellite imagery of climatic indices (deep neural networks) compared with non-raster variables (gradient boosting machine). Results: Moran's I estimation, confirmed by the Monte Carlo approximation indicated evidence of significant spatial autocorrelation with Moran's I values of 0.0561 (P < 0.00259) and 0.0561 (P < 0.009) respectively while significant clustering was observed in regions of northern Italy. The ML-based deep neural network yielded an accuracy of 80% and 79.7% for NDVI- and LST-based raster inputs, while GBM model isolated key predictors of human WNV confirmed cases with an AUC of 0.835. Conclusions: This study presents a scalable approach for human WNV outbreak prediction incorporating geo-spatial analyses and artificial intelligence, providing insights on the disease epidemiology and a framework for other emerging infectious disease investigations