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Construction of a Seasonal Difference-Geographically and Temporally Weighted Regression (SD-GTWR) Model and Comparative Analysis with GWR-Based Models for Hemorrhagic Fever with Renal Syndrome (HFRS) in Hubei Province (China)

Author

Listed:
  • Liang Ge

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
    Tianjin Institute of Surveying and Mapping, Tianjin 300381, China)

  • Youlin Zhao

    (Business School of Hohai University, Nanjing 211100, China)

  • Zhongjie Sheng

    (Tianjin Institute of Surveying and Mapping, Tianjin 300381, China)

  • Ning Wang

    (First Crust Deformation Monitoring and Application Center, China Earthquake Administration, Tianjin 300180, China)

  • Kui Zhou

    (Tianjin Institute of Surveying and Mapping, Tianjin 300381, China)

  • Xiangming Mu

    (School of Information Studies of University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA)

  • Liqiang Guo

    (Tianjin Institute of Surveying and Mapping, Tianjin 300381, China)

  • Teng Wang

    (Business School of Hohai University, Nanjing 211100, China)

  • Zhanqiu Yang

    (State Key Laboratory of Virology, Institute of Medical Virology, School of Medicine, Wuhan University, Wuhan 430079, China)

  • Xixiang Huo

    (Hubei Provincial Center for Disease Control and Prevention, Wuhan 430079, China)

Abstract

Hemorrhagic fever with renal syndrome (HFRS) is considered a globally distributed infectious disease which results in many deaths annually in Hubei Province, China. In order to conduct a better analysis and accurately predict HFRS incidence in Hubei Province, a new model named Seasonal Difference-Geographically and Temporally Weighted Regression (SD-GTWR) was constructed. The SD-GTWR model, which integrates the analysis and relationship of seasonal difference, spatial and temporal characteristics of HFRS (HFRS was characterized by spatiotemporal heterogeneity and it is seasonally distributed), was designed to illustrate the latent relationships between the spatio-temporal pattern of the HFRS epidemic and its influencing factors. Experiments from the study demonstrated that SD-GTWR model is superior to traditional models such as GWR- based models in terms of the efficiency and the ability of providing influencing factor analysis.

Suggested Citation

  • Liang Ge & Youlin Zhao & Zhongjie Sheng & Ning Wang & Kui Zhou & Xiangming Mu & Liqiang Guo & Teng Wang & Zhanqiu Yang & Xixiang Huo, 2016. "Construction of a Seasonal Difference-Geographically and Temporally Weighted Regression (SD-GTWR) Model and Comparative Analysis with GWR-Based Models for Hemorrhagic Fever with Renal Syndrome (HFRS) ," IJERPH, MDPI, vol. 13(11), pages 1-14, October.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:11:p:1062-:d:81707
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    References listed on IDEAS

    as
    1. Shujuan Li & Hongyan Ren & Wensheng Hu & Liang Lu & Xinliang Xu & Dafang Zhuang & Qiyong Liu, 2014. "Spatiotemporal Heterogeneity Analysis of Hemorrhagic Fever with Renal Syndrome in China Using Geographically Weighted Regression Models," IJERPH, MDPI, vol. 11(12), pages 1-19, November.
    2. Tomoki Nakaya & Katsumi Nakase & Ken Osaka, 2005. "Spatio-temporal modelling of the HIV epidemic in Japan based on the national HIV/AIDS surveillance," Journal of Geographical Systems, Springer, vol. 7(3), pages 313-336, December.
    3. Changjun Bao & Wanwan Liu & Yefei Zhu & Wendong Liu & Jianli Hu & Qi Liang & Yuejia Cheng & Ying Wu & Rongbin Yu & Minghao Zhou & Hongbing Shen & Feng Chen & Fenyang Tang & Zhihang Peng, 2014. "The Spatial Analysis on Hemorrhagic Fever with Renal Syndrome in Jiangsu Province, China Based on Geographic Information System," PLOS ONE, Public Library of Science, vol. 9(9), pages 1-8, September.
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    Cited by:

    1. Shujuan Li & Lingli Zhu & Lidan Zhang & Guoyan Zhang & Hongyan Ren & Liang Lu, 2023. "Urbanization-Related Environmental Factors and Hemorrhagic Fever with Renal Syndrome: A Review Based on Studies Taken in China," IJERPH, MDPI, vol. 20(4), pages 1-20, February.
    2. Qing Wang & Yuhang Xiao, 2022. "Has Urban Construction Land Achieved Low-Carbon Sustainable Development? A Case Study of North China Plain, China," Sustainability, MDPI, vol. 14(15), pages 1-29, August.
    3. Sui Zhang & Minghao Wang & Zhao Yang & Baolei Zhang, 2021. "A Novel Predictor for Micro-Scale COVID-19 Risk Modeling: An Empirical Study from a Spatiotemporal Perspective," IJERPH, MDPI, vol. 18(24), pages 1-16, December.

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