Multiscale environmental drivers of human brucellosis transmission in Xinjiang: A spatiotemporal analysis integrating GAM and MaxEnt modeling (2015-2023).

This study investigated the epidemiological characteristics of human brucellosis and the associations with meteorological, environmental, and socio-economic factors in Xinjiang Uygur Autonomous Region (XUAR), China, between 2015 and 2023. Using a Generalized Additive Model (GAM), we analyzed nonlinear associations between meteorological variables and case counts, incorporating optimized lag periods for each climatic factor. The Maximum Entropy (MaxEnt) model was simultaneously applied to evaluate the synergistic effects of environmental and socio-economic determinants on disease distribution patterns throughout the region. Key findings revealed distinct epidemiological patterns, characterized by an initial decline followed by a resurgence in cases, predominantly among males (71.2%) and older age groups. Meteorological analysis identified temperature, precipitation, and wind speed as significant risk factors with time-lagged effects, while higher humidity demonstrated a protective effect. Spatially, population density and vegetation cover were the strongest predictors of disease distribution, with high-risk areas concentrated in central and western XUAR, particularly urban centers such as Urumqi and Kashgar. The models demonstrated strong predictive performance, with MaxEnt achieving an area under the curve (AUC) value of 0.987. These findings highlight the complex interplay of climatic, ecological, and demographic factors in brucellosis transmission. The study recommends enhanced surveillance in high-risk regions, implementation of weather-based early warning systems, and targeted livestock control measures in areas with characteristic environmental risk factors.