ArticleThe Lancet regional health. Western Pacific2025
Combined effects of hydrological conditions and socioeconomic factors on the seasonal dynamics of severe fever with thrombocytopenia syndrome in China, 2011-2022: a modelling study.
Article in The Lancet regional health. Western Pacific, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Meteorological and environmental factors associated with the spatiotemporal risk of severe fever with thrombocytopenia syndrome in Japan: A prefecture-level time-series modeling study.One health (Amsterdam, Netherlands) · 2026Article
- Complex exposure-response relationships between meteorological factors and severe fever with thrombocytopenia syndrome risk.Tropical medicine and health · 2026Article
- From qualitative prediction to quantitative insight: combined meteorological patterns and regional dynamics of severe fever with thrombocytopenia syndrome in Liaoning Province, China, 2010-2024.Infectious Disease Modelling · 2026Article
- Environmental, climatic, and social risk factors of severe fever with thrombocytopenia syndrome and the implications of climate change.One health (Amsterdam, Netherlands) · 2026Article
- The effect of meteorological factors on severe fever with thrombocytopenia syndrome: Evidence from 34 Chinese cities.One health (Amsterdam, Netherlands) · 2026Article
- Association between hydrometeorological conditions and hemorrhagic fever with renal syndrome in Shandong Province, China, from 2005 to 2019.PLoS neglected tropical diseases · 2025Article
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Severe fever with thrombocytopenia syndrome (SFTS) is a tick-borne viral hemorrhagic fever with expanding geographical range. The determinants of the seasonal dynamics of SFTS remain poorly understood. Methods: Monthly SFTS cases from 604 counties in five provinces with high-notification rate in China (2011-2022) were analyzed using hierarchical Bayesian spatiotemporal and distributed lag nonlinear models. Cumulative and month-specific effects of meteorological factors were assessed, with socioeconomic factors as modifiers. Findings: The cumulative effect peaked at 21.97 °C ( Interpretation: Climatic factors significantly influence SFTS dynamics, with socioeconomic conditions modifying these effects. Integrating climate factors into surveillance and early warning systems is essential for targeted prevention and control. Funding: National Natural Science Foundation of China (No. 82330103 and No. 42201497), Youth Innovation Promotion Association (No. 2023000117), and the Wellcome Trust [220211].
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