Modeling the role of environmental and socioeconomic factors in endemic SFTS risk using a multicomponent framework.

Background: Severe fever with thrombocytopenia syndrome (SFTS) is a rapidly emerging tick-borne disease with high mortality. Despite increasing recognition of environmental and socioeconomic drivers, their roles in sustaining local transmission remain poorly differentiated. Methods: We collected county-level SFTS case data from Shandong Province between 2015 and 2022, along with corresponding socioeconomic and meteorological indicators. A random forest regression (RFE) model was used to quantify the relative importance of environmental and social drivers. Additionally, a spatiotemporal multi-component (STM) model was employed at the county level to characterize transmission heterogeneity and key driving forces. Results: SFTS incidence exhibited pronounced spatiotemporal heterogeneity, with Jiaodong Peninsula and central Shandong identified as major hotspots. The epidemic component (0.59, 95%CI: 0.50, 0.76) predominated in coastal areas, while the endemic risk was more uniformly distributed (0.03, 95%CI: 0.01, 0.17). Maximum temperature (1.69, 95%CI: 0.10, 3.78) and relative humidity (1.67, 95%CI: 0.90, 2.88) was positively associated with endemic risk, whereas higher population density (-0.72, 95%CI: -0.91, -0.57) and wind speed (-1.19, 95%CI: -1.99, -0.33) were inversely associated. Incorporating these covariates significantly improved model fit and reduced unexplained heterogeneity in endemic risk. Three distinct transmission patterns were identified across high-incidence counties, reflecting dynamic shifts in dominant transmission modes over time. Conclusions: Environmental and demographic factors jointly influence SFTS transmission in Shandong, with higher maximum temperature promoting its spread, whereas higher population density and stronger wind speed exert inhibitory effects. These findings emphasize the necessity of developing region-specific surveillance and intervention strategies that account for local environmental conditions and transmission patterns to effectively mitigate disease risk.