One health (Amsterdam, Netherlands) · 2026
Environmental, climatic, and social risk factors of severe fever with thrombocytopenia syndrome and the implications of climate change.
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Yang Yating, Ren Jiangping, Qin Guoyou, Lu Yihan, Liu Ying, Sun Jimin, Yao Ye, Liu Yang
Аннотация
BACKGROUND: SFTS is a tick-borne infectious disease with a high case fatality ratio with geographic expansion. While there have been some studies exploring the link between environmental and climatic factors on SFTS, we further test a hypothesis that social factors may play a role in the increasing burden.
METHODS: Using data from Zhejiang Province, China (2011-2023), we estimated the effects of environmental, climatic, and social factors on SFTS incidence using a Bayesian negative binomial regression model. Rigorous model selection processes, including variations of functional formats among independent variables and model validation, were followed. Influences of each covariate were assessed using block cross-validation experiments. We predicted the implications of the associations derived until 2028 based on climate projections.
FINDINGS: Environmental, climatic, and social factors were all selected in the final model. Higher risk areas included mountainous and cropland regions, with moderate temperatures, high precipitation, and longer sunlight exposure. Urbanization is negatively associated with SFTS risks, while human mobility is positively associated with SFTS risks. Spatial random effects do not significantly improve model performance after including human mobility measures and were thus excluded from the final model. By 2028, we are expecting a 23% increase in SFTS incidence; 9% more counties are likely going to report their first case. An SFTS season is expected to expand from March to October to year-round.
CONCLUSIONS: This study provides a comprehensive investigation of the associations between environmental, climatic, and social factors and SFTS risks using a data-driven approach. Our findings underscore the growing role of social dynamics, particularly mobility, in shaping SFTS risk. SFTS in Zhejiang is expected to spread wider and last longer, calling for timely adaptation of public health strategies.
METHODS: Using data from Zhejiang Province, China (2011-2023), we estimated the effects of environmental, climatic, and social factors on SFTS incidence using a Bayesian negative binomial regression model. Rigorous model selection processes, including variations of functional formats among independent variables and model validation, were followed. Influences of each covariate were assessed using block cross-validation experiments. We predicted the implications of the associations derived until 2028 based on climate projections.
FINDINGS: Environmental, climatic, and social factors were all selected in the final model. Higher risk areas included mountainous and cropland regions, with moderate temperatures, high precipitation, and longer sunlight exposure. Urbanization is negatively associated with SFTS risks, while human mobility is positively associated with SFTS risks. Spatial random effects do not significantly improve model performance after including human mobility measures and were thus excluded from the final model. By 2028, we are expecting a 23% increase in SFTS incidence; 9% more counties are likely going to report their first case. An SFTS season is expected to expand from March to October to year-round.
CONCLUSIONS: This study provides a comprehensive investigation of the associations between environmental, climatic, and social factors and SFTS risks using a data-driven approach. Our findings underscore the growing role of social dynamics, particularly mobility, in shaping SFTS risk. SFTS in Zhejiang is expected to spread wider and last longer, calling for timely adaptation of public health strategies.