ArticleInternational journal of environmental research and public health2016
Spatial Analysis of Severe Fever with Thrombocytopenia Syndrome Virus in China Using a Geographically Weighted Logistic Regression Model.
Article in International journal of environmental research and public health, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- Systematic review of environmental factors associated with severe fever with thrombocytopenia syndrome occurrences.Epidemiologic reviews · 2026Pooled it
- 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
- Escalating transmissibility of severe fever with thrombocytopenia syndrome in a high-endemic region of China.Nature communications · 2026Article
- Clinical value of coagulation parameters in predicting the severity of severe fever with thrombocytopenia syndrome.Frontiers in microbiology · 2024Article
- Spatiotemporal distribution and environmental influences of severe fever with thrombocytopenia syndrome in Shandong Province, China.BMC infectious diseases · 2023Article
- A spatial analysis of TB cases and abnormal X-rays detected through active case-finding in Karachi, Pakistan.Scientific reports · 2023Article
- Epidemiological characteristics of severe fever with thrombocytopenia syndrome and its relationship with meteorological factors in Liaoning Province, China.Parasites & vectors · 2022Article
- Local neural-network-weighted models for occurrence and number of down wood in natural forest ecosystem.Scientific reports · 2022Article
- Web-Based Skin Cancer Assessment and Classification Using Machine Learning and Mobile Computerized Adaptive Testing in a Rasch Model: Development Study.JMIR medical informatics · 2022Article
- Spatial-temporal characteristics of severe fever with thrombocytopenia syndrome and the relationship with meteorological factors from 2011 to 2018 in Zhejiang Province, China.PLoS neglected tropical diseases · 2020Article
- Spatial epidemiological determinants of severe fever with thrombocytopenia syndrome in Miyazaki, Japan: a GWLR modeling study.BMC infectious diseases · 2019Article
- Geographically weighted temporally correlated logistic regression model.Scientific reports · 2018Article
- Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health: An Editorial.International journal of environmental research and public health · 2017Article
- Isolation, characterization, and phylogenic analysis of three new severe fever with thrombocytopenia syndrome bunyavirus strains derived from Hubei Province, China.Virologica Sinica · 2017Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Severe fever with thrombocytopenia syndrome (SFTS) is caused by severe fever with thrombocytopenia syndrome virus (SFTSV), which has had a serious impact on public health in parts of Asia. There is no specific antiviral drug or vaccine for SFTSV and, therefore, it is important to determine the factors that influence the occurrence of SFTSV infections. This study aimed to explore the spatial associations between SFTSV infections and several potential determinants, and to predict the high-risk areas in mainland China. The analysis was carried out at the level of provinces in mainland China. The potential explanatory variables that were investigated consisted of meteorological factors (average temperature, average monthly precipitation and average relative humidity), the average proportion of rural population and the average proportion of primary industries over three years (2010-2012). We constructed a geographically weighted logistic regression (GWLR) model in order to explore the associations between the selected variables and confirmed cases of SFTSV. The study showed that: (1) meteorological factors have a strong influence on the SFTSV cover; (2) a GWLR model is suitable for exploring SFTSV cover in mainland China; (3) our findings can be used for predicting high-risk areas and highlighting when meteorological factors pose a risk in order to aid in the implementation of public health strategies.
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