Evidence map›Paper›PMID 42666519›Full record

ArticleOne health (Amsterdam, Netherlands)2026

Meteorological and environmental factors associated with the spatiotemporal risk of severe fever with thrombocytopenia syndrome in Japan: A prefecture-level time-series modeling study.

Fangyu Yan, Sophearen Ith, Noriko Kitamura, Yu Takizawa, Taro Kamigaki, Motoi Suzuki, Ken Maeda, Daisuke Yoneoka

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. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

8 authors.

Fangyu YanDepartment of Infectious Disease Surveillance, National Institute of Infectious Diseases, Japan Institute for Health Security, Tokyo, Japan.
Sophearen IthDepartment of Epidemiology, National Institute of Infectious Diseases, Japan Institute for Health Security, Tokyo, Japan.
Noriko KitamuraCenter for Infectious Disease Epidemiology, National Institute of Infectious Diseases, Japan Institute for Health Security, Tokyo, Japan.
Yu TakizawaDepartment of Infectious Disease Surveillance, National Institute of Infectious Diseases, Japan Institute for Health Security, Tokyo, Japan.
Taro KamigakiDepartment of Infectious Disease Surveillance, National Institute of Infectious Diseases, Japan Institute for Health Security, Tokyo, Japan.
Motoi SuzukiDepartment of Epidemiology, National Institute of Infectious Diseases, Japan Institute for Health Security, Tokyo, Japan.
Ken MaedaDepartment of Veterinary Science, National Institute of Infectious Diseases, Japan Institute for Health Security, Tokyo, Japan.
Daisuke YoneokaDepartment of Epidemiology, National Institute of Infectious Diseases, Japan Institute for Health Security, Tokyo, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Severe fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne zoonotic disease with a high case fatality rate and expanding geographic distribution in Japan. Comprehensive evaluations that integrate meteorological, environmental, and demographic factors remain limited. This study aimed to quantify these associations. Methods: We conducted a nationwide prefecture-level time-series analysis using weekly SFTS surveillance data in Japan from March 2013 to December 2025. A generalized additive mixed model adjusting for nonlinear seasonality and long-term trends was used to assess (1) the associations between SFTS risk and meteorological, environmental and demographic factors, and (2) the value of the factor at which risk become highest. Results: A total of 1237 SFTS cases were reported. Mean temperature and precipitation showed inverted U-shaped associations with SFTS risk, with peak risks observed at 21.8 °C (relative risk [RR]: 1.78, 95% confidence interval [CI]: 1.26-2.52) and 156.6 mm (RR: 1.30, 95% CI: 1.04-1.62), respectively. Atmospheric pressure and forest coverage were positively associated with SFTS risk, with maximum RRs of 2.06 (95% CI: 1.44-2.93) at 1027.1 hPa and 6.58 (95% CI: 4.95-8.74) at 0.84, respectively. Mean altitude showed an inverse association, with the highest RR observed at 45.0 m (RR: 3.99, 95% CI: 1.81-8.76). Conclusions: The increasing number and geographic expansion of SFTS cases highlight the growing public health importance of this disease in Japan. Our findings improve understanding of factors associated with SFTS transmission and may inform future risk assessment and public health preparedness.

Indexed as

Environmental factorsGeneralized additive modelMeteorological factorsNon-linear associationSevere fever with thrombocytopenia syndromeTick-borne disease

Identifiers

PMID42666519
PMCPMC13522366

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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.