Exploratory Associations Between Climatic, Environmental, and Surveillance Indicators and Human West Nile Virus Infections in Apulia: A Bayesian Spatio-Temporal Analysis.

West Nile virus (WNV) transmission has intensified and expanded in Italy, but quantitative evidence on how local climatic and environmental conditions influence human risk in southern regions remains limited. This study examined the association between meteorological, environmental, and host-related factors and West Nile virus (WNV) cases at the municipal level in Apulia in 2023. Human WNV cases were georeferenced at the municipal level and linked to monthly indicators (minimum and maximum temperature, precipitation, surface water extent, green area coverage, land-use change, avian occurrence). A Bayesian spatio-temporal Poisson model with a conditional autoregressive structure was fitted to monthly counts of human WNV cases, with equine WNV cases, climatic, environmental and avian indicators included as covariates. Eight human WNV cases were reported between August and October and four equine WNV cases between September and November, with partial spatial and temporal overlap. In univariable analyses, the strongest associations were observed for meteorological variables, particularly temperature. In the final multivariable model, higher maximum temperature at a two-month lag was associated with increased WNV risk (RR = 1.53; 95% CrI: 1.18-2.23), while minimum temperature was excluded due to collinearity with maximum temperature. Green area coverage and water body extent showed uncertain effects. Model-based maps indicated that elevated fitted risk was concentrated in a narrow temporal window between August and October, expanding sharply across the region in September before receding, rather than describing a stable, spatially fixed hotspot. This exploratory analysis suggests that reported human WNV infections in Apulia in 2023 were temporally concentrated during the late summer/early autumn period and that maximum temperature at a two-month lag was positively associated with the outcome in the selected model. The findings are hypothesis-generating and should be interpreted with caution given the very small number of events, but they illustrate the feasibility of integrating multisource epidemiological, climatic, environmental, and veterinary data to support locally tailored early-warning efforts in southern Italy.