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Ecological Niche Modeling of Risk Factors for H7N9 Human Infection in China

Author

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  • Min Xu

    (State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China)

  • Chunxiang Cao

    (State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China)

  • Qun Li

    (Public Health Emergency Center, Chinese Center for Disease Control and Prevention, Beijing 102206, China)

  • Peng Jia

    (Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede 7500, The Netherlands
    Department of Epidemiology and Environmental Health, University at Buffalo, Buffalo, NY 14214, USA)

  • Jian Zhao

    (Public Health Emergency Center, Chinese Center for Disease Control and Prevention, Beijing 102206, China)

Abstract

China was attacked by a serious influenza A (H7N9) virus in 2013. The first human infection case was confirmed in Shanghai City and soon spread across most of eastern China. Using the methods of Geographic Information Systems (GIS) and ecological niche modeling (ENM), this research quantitatively analyzed the relationships between the H7N9 occurrence and the main environmental factors, including meteorological variables, human population density, bird migratory routes, wetland distribution, and live poultry farms, markets, and processing factories. Based on these relationships the probability of the presence of H7N9 was predicted. Results indicated that the distribution of live poultry processing factories, farms, and human population density were the top three most important determinants of the H7N9 human infection. The relative contributions to the model of live poultry processing factories, farms and human population density were 39.9%, 17.7% and 17.7%, respectively, while the maximum temperature of the warmest month and mean relative humidity had nearly no contribution to the model. The paper has developed an ecological niche model (ENM) that predicts the spatial distribution of H7N9 cases in China using environmental variables. The area under the curve (AUC) values of the model were greater than 0.9 (0.992 for the training samples and 0.961 for the test data). The findings indicated that most of the high risk areas were distributed in the Yangtze River Delta. These findings have important significance for the Chinese government to enhance the environmental surveillance at multiple human poultry interfaces in the high risk area.

Suggested Citation

  • Min Xu & Chunxiang Cao & Qun Li & Peng Jia & Jian Zhao, 2016. "Ecological Niche Modeling of Risk Factors for H7N9 Human Infection in China," IJERPH, MDPI, vol. 13(6), pages 1-12, June.
  • Handle: RePEc:gam:jijerp:v:13:y:2016:i:6:p:600-:d:72156
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    References listed on IDEAS

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    1. Sara Amirpour Haredasht & Miguel Barrios & Jamshid Farifteh & Piet Maes & Jan Clement & Willem W. Verstraeten & Katrien Tersago & Marc Van Ranst & Pol Coppin & Daniel Berckmans & Jean-Marie Aerts, 2013. "Ecological Niche Modelling of Bank Voles in Western Europe," IJERPH, MDPI, vol. 10(2), pages 1-16, January.
    2. Peter Horby, 2013. "H7N9 is a virus worth worrying about," Nature, Nature, vol. 496(7446), pages 399-399, April.
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    Cited by:

    1. Qinling Yan & Sanyi Tang & Zhen Jin & Yanni Xiao, 2019. "Identifying Risk Factors Of A(H7N9) Outbreak by Wavelet Analysis and Generalized Estimating Equation," IJERPH, MDPI, vol. 16(8), pages 1-13, April.
    2. Peter Congdon, 2016. "Spatiotemporal Frameworks for Infectious Disease Diffusion and Epidemiology," IJERPH, MDPI, vol. 13(12), pages 1-4, December.
    3. Wen Dong & Peng Zhang & Quan-Li Xu & Zhong-Da Ren & Jie Wang, 2022. "A Study on a Neural Network Risk Simulation Model Construction for Avian Influenza A (H7N9) Outbreaks in Humans in China during 2013–2017," IJERPH, MDPI, vol. 19(17), pages 1-16, August.
    4. Yongqing Zhao & Rendong Li & Juan Qiu & Xiangdong Sun & Lu Gao & Mingquan Wu, 2019. "Prediction of Human Brucellosis in China Based on Temperature and NDVI," IJERPH, MDPI, vol. 16(21), pages 1-15, November.

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    More about this item

    Keywords

    avian flu; H7N9; environmental factors; spatial modeling; MaxEnt;
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