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Spatial Analysis of Severe Fever with Thrombocytopenia Syndrome Virus in China Using a Geographically Weighted Logistic Regression Model

Author

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  • Liang Wu

    (School of Information Engineering, China University of Geosciences, Wuhan 430074, China
    National Engineering Research Center for GIS, Wuhan 430074, China)

  • Fei Deng

    (State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan 430071, China)

  • Zhong Xie

    (School of Information Engineering, China University of Geosciences, Wuhan 430074, China
    National Engineering Research Center for GIS, Wuhan 430074, China)

  • Sheng Hu

    (School of Information Engineering, China University of Geosciences, Wuhan 430074, China)

  • Shu Shen

    (State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan 430071, China)

  • Junming Shi

    (State Key Laboratory of Virology, Wuhan Institute of Virology, Chinese Academy of Sciences, Wuhan 430071, China)

  • Dan Liu

    (School of Medicine, Wuhan University of Science and Technology, Wuhan 430081, China)

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.

Suggested Citation

  • Liang Wu & Fei Deng & Zhong Xie & Sheng Hu & Shu Shen & Junming Shi & Dan Liu, 2016. "Spatial Analysis of Severe Fever with Thrombocytopenia Syndrome Virus in China Using a Geographically Weighted Logistic Regression Model," IJERPH, MDPI, vol. 13(11), pages 1-16, November.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:11:p:1125-:d:82707
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    References listed on IDEAS

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    1. David Wheeler & Michael Tiefelsdorf, 2005. "Multicollinearity and correlation among local regression coefficients in geographically weighted regression," Journal of Geographical Systems, Springer, vol. 7(2), pages 161-187, June.
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    Cited by:

    1. Jamal Jokar Arsanjani, 2017. "Remote Sensing, Crowd Sensing, and Geospatial Technologies for Public Health: An Editorial," IJERPH, MDPI, vol. 14(4), pages 1-3, April.

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