Speaker
Description
Severe fever with thrombocytopenia syndrome (SFTS) is a tick-borne viral disease primarily reported in East Asia. In the Republic of Korea, the disease burden is concentrated among older adults, who experience higher incidence and case fatality rates. Meanwhile, Korea is undergoing demographic aging alongside environmental changes associated with climate change. These shifts raise important questions about future SFTS risk.
In this study, we developed long-term predictive models to evaluate how population aging and climate change may influence future SFTS incidence. Using nationwide epidemiological surveillance data with meteorological, demographic, and behavioral information, we predicted both tick density and SFTS incidence among individuals aged 60 years and older using machine learning models, including linear regression, XGBoost, and deep neural networks. The best-performing model was then used to generate long-term predictions of SFTS incidence under future demographic projections and climate change scenarios.
Model predictions suggest that SFTS incidence among older adults in Korea will increase in the coming decades, with larger increases under high-emission scenarios. The results indicate that demographic aging and climate change may act together to amplify future SFTS risk in elderly populations. These findings emphasize the need to consider both demographic and environmental factors when developing strategies for vector-borne disease preparedness and control.
