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Research on Quantitative Analysis of Multiple Factors Affecting COVID-19 Spread

Author

Listed:
  • Yu Fu

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China)

  • Shaofu Lin

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
    Beijing Institute of Smart City, Beijing University of Technology, Beijing 100124, China)

  • Zhenkai Xu

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China)

Abstract

The Corona Virus Disease 2019 (COVID-19) is spreading all over the world. Quantitative analysis of the effects of various factors on the spread of the epidemic will help people better understand the transmission characteristics of SARS-CoV-2, thus providing a theoretical basis for governments to develop epidemic prevention and control strategies. This article uses public data sets from The Center for Systems Science and Engineering at Johns Hopkins University (JHU CSSE), Air Quality Open Data Platform, China Meteorological Data Network, and WorldPop website to construct experimental data. The epidemic situation is predicted by Dual-link BiGRU Network, and the relationship between epidemic spread and various feature factors is quantitatively analyzed by the Gauss-Newton iteration Method. The study found that population density has the greatest positive correlation to the spread of the epidemic among the selected feature factors, followed by the number of landing flights. The number of newly diagnosed daily will increase by 1.08% for every 1% of the population density, the number of newly diagnosed daily will increase by 0.98% for every 1% of the number of landing flights. The results of this study show that the control of social distance and population movement has a high priority in epidemic prevention and control strategies, and it can play a very important role in controlling the spread of the epidemic.

Suggested Citation

  • Yu Fu & Shaofu Lin & Zhenkai Xu, 2022. "Research on Quantitative Analysis of Multiple Factors Affecting COVID-19 Spread," IJERPH, MDPI, vol. 19(6), pages 1-13, March.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:6:p:3187-:d:766764
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    References listed on IDEAS

    as
    1. Shaofu Lin & Yu Fu & Xiaofeng Jia & Shimin Ding & Yongxing Wu & Zhou Huang, 2020. "Discovering Correlations between the COVID-19 Epidemic Spread and Climate," IJERPH, MDPI, vol. 17(21), pages 1-14, October.
    2. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    3. Zeroual, Abdelhafid & Harrou, Fouzi & Dairi, Abdelkader & Sun, Ying, 2020. "Deep learning methods for forecasting COVID-19 time-Series data: A Comparative study," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    4. Elizabeth L. Anderson & Paul Turnham & John R. Griffin & Chester C. Clarke, 2020. "Consideration of the Aerosol Transmission for COVID‐19 and Public Health," Risk Analysis, John Wiley & Sons, vol. 40(5), pages 902-907, May.
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    Cited by:

    1. Yu-Tse Tsan & Endah Kristiani & Po-Yu Liu & Wei-Min Chu & Chao-Tung Yang, 2022. "In the Seeking of Association between Air Pollutant and COVID-19 Confirmed Cases Using Deep Learning," IJERPH, MDPI, vol. 19(11), pages 1-19, May.
    2. Xiaodong Zhang & Haoying Han, 2023. "Spatiotemporal Dynamic Characteristics and Causes of China’s Population Aging from 2000 to 2020," Sustainability, MDPI, vol. 15(9), pages 1-19, April.

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