Spatial-temporal distribution characteristics of haemorrhagic fever with renal syndrome (HFRS) in 31 provinces and municipalities of P.R. China and multi-factorial prediction of incidence.

OBJECTIVE: To integrate multi-source spatiotemporal data and construct an HFRS incidence prediction model using XGBoost with SHAP, exploring factors associated with HFRS incidence in China. METHODS: Collected HFRS case data from 31 provinces in China (2005-2020) and multi-source data on climate, land use, and population. Used MaxEnt to analyze rodent distribution and key environmental variables. Constructed an XGBoost model integrating climate, land use, host, and socio-economic features, with SHAP values assessing feature contributions. Dataset divided into training (70%), validation (15%), and test (15%) sets, with higher weights for high-incidence provinces. RESULTS: The reported HFRS cases in mainland China decreased from 2005 to 2020 but showed seasonal peaks. Cases were mainly among adults aged 20-60 years. The distribution of virus-carrying rodents was mainly affected by minimum temperature of the coldest month and elevation. After removing temporal features, land-use types (forest, cropland, shrub) showed greater relative importance than climate variables. CONCLUSION: Forest, shrub, and cropland may affect disease transmission via host distribution and food availability. Climate factors act indirectly on HFRS risk. Temporal features remain critical for incidence prediction, while land use and rodent habitats are key exogenous drivers. These findings support targeted public health strategies.