Predicting risks of hemorrhagic fever with renal syndrome using a Bayesian spatiotemporal hierarchical model

Hemorrhagic fever with renal syndrome (HFRS), a severe infectious disease primarily hosted by rodents, poses significant public health challenges in endemic regions. However, the intricate interplay of factors driving HFRS transmission, characterized by spatiotemporal heterogeneity and nonlinear relationships across different urbanization contexts, remains insufficiently explored. Utilizing historical HFRS cases, remote sensing imagery, and census data, this study constructed a Bayesian spatiotemporal hierarchical model to reveal the multiple risk drivers of HFRS transmission in the Guanzhong Plain, China. By integrating structured space-time components, nonlinear smoothing functions, and spatiotemporally varying coefficients, this model effectively captures the dynamic, non-stationary, and regionally heterogeneous nature of HFRS transmission. In addition, we constructed three integrated indices, the Human Activity Intensity Index (HAI), Eco-environmental Quality Index (EQI), and Habitat Suitability Index (HSI) to quantify the synergistic effects of human-environment systems. The model demonstrated strong performance (R² = 0.814), identifying host habitat suitability as the dominant risk driver (RR = 2.283). Results further elucidated how meteorological conditions, ecological quality, and human activities influence HFRS risk across urbanization gradients. This study provides a generalized Bayesian framework for investigating geospatial health issues characterized by instability and complex nonlinear relationships.