Environmental, climatic, and social risk factors of severe fever with thrombocytopenia syndrome and the implications of climate change.

Yating Yang, Jiangping Ren, Guoyou Qin, Yihan Lu, Ying Liu, Jimin Sun, Ye Yao, Yang Liu

Abstract read

In one paragraph

Article in One health (Amsterdam, Netherlands), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it

0cells of the map it votes in

2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

Complex exposure-response relationships between meteorological factors and severe fever with thrombocytopenia syndrome risk.Tropical medicine and health · 2026
Article
Genetic characterization of severe fever with thrombocytopenia syndrome virus in ticks from livestock environments in South Korea.Frontiers in microbiology · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Yating YangDepartment of Biostatistics, National Commission of Health Key Laboratory for Health Technology Assessment, and the Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai, China.

Jiangping RenZhejiang Key Lab of Vaccine, Infectious Disease Prevention and Control, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China.

Guoyou QinDepartment of Biostatistics, National Commission of Health Key Laboratory for Health Technology Assessment, and the Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai, China.

Yihan LuShanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China.

Ying LiuZhejiang Key Lab of Vaccine, Infectious Disease Prevention and Control, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China.

Jimin SunZhejiang Key Lab of Vaccine, Infectious Disease Prevention and Control, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, China.

Ye YaoDepartment of Biostatistics, National Commission of Health Key Laboratory for Health Technology Assessment, and the Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai, China.

Yang LiuPATH, London, United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Climate changeHuman mobilityProjectionsSevere fever with thrombocytopenia syndromeSocio-environmental factors

Identifiers

PMID42094846

DOI10.1016/j.onehlt.2026.101430

PMCPMC13141556

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LicenceCC BY

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.