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A Study on a Neural Network Risk Simulation Model Construction for Avian Influenza A (H7N9) Outbreaks in Humans in China during 2013–2017

Author

Listed:
  • Wen Dong

    (Faculty of Geography, Yunnan Normal University, Kunming 650500, China
    GIS Technology Engineering Research Centre for West-China Resources and Environment of Educational Ministry, Yunnan Normal University, Kunming 650500, China)

  • Peng Zhang

    (College of Intelligent Information Engineering, Chongqing Aerospace Polytechnic College, Chongqing 400021, China)

  • Quan-Li Xu

    (Faculty of Geography, Yunnan Normal University, Kunming 650500, China
    GIS Technology Engineering Research Centre for West-China Resources and Environment of Educational Ministry, Yunnan Normal University, Kunming 650500, China)

  • Zhong-Da Ren

    (GIS Technology Engineering Research Centre for West-China Resources and Environment of Educational Ministry, Yunnan Normal University, Kunming 650500, China
    State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, China)

  • Jie Wang

    (Chongqing City Management College, Chongqing 401331, China)

Abstract

The main purposes of this study were to explore the spatial distribution characteristics of H7N9 human infections during 2013–2017, and to construct a neural network risk simulation model of H7N9 outbreaks in China and evaluate their effects. First, ArcGIS 10.6 was used for spatial autocorrelation analysis, and cluster patterns ofH7N9 outbreaks were analyzed in China during 2013–2017 to detect outbreaks’ hotspots. During the study period, the incidence of H7N9 outbreaks in China was high in the eastern and southeastern coastal areas of China, with a tendency to spread to the central region. Moran’s I values of global spatial autocorrelation of H7N9 outbreaks in China from 2013 to 2017 were 0.080128, 0.073792, 0.138015, 0.139221 and 0.050739, respectively ( p < 0.05) indicating a statistically significant positive correlation of the epidemic. Then, SPSS 20.0 was used to analyze the correlation between H7N9 outbreaks in China and population, livestock production, the distance between the case and rivers, poultry farming, poultry market, vegetation index, etc. Statistically significant influencing factors screened out by correlation analysis were population of the city, average vegetation of the city, and the distance between the case and rivers ( p < 0.05), which were included in the neural network risk simulation model of H7N9 outbreaks in China. The simulation accuracy of the neural network risk simulation model of H7N9 outbreaks in China from 2013 to 2017 were 85.71%, 91.25%, 91.54%, 90.49% and 92.74%, and the AUC were 0.903, 0.976, 0.967, 0.963 and 0.970, respectively, showing a good simulation effect of H7N9 epidemics in China. The innovation of this study lies in the epidemiological study of H7N9 outbreaks by using a variety of technical means, and the construction of a neural network risk simulation model of H7N9 outbreaks in China. This study could provide valuable references for the prevention and control of H7N9 outbreaks in China.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:17:p:10877-:d:903139
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    References listed on IDEAS

    as
    1. 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.
    2. Din, Anwarud & Li, Yongjin & Khan, Tahir & Zaman, Gul, 2020. "Mathematical analysis of spread and control of the novel corona virus (COVID-19) in China," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    3. Fabio Baione & Davide Biancalana & Paolo Angelis, 2021. "An application of Sigmoid and Double-Sigmoid functions for dynamic policyholder behaviour," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(1), pages 5-22, June.
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    More about this item

    Keywords

    H7N9; GIS; spatial analysis; risk factors; risk simulation model;
    All these keywords.

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