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Effects of Meteorological Factors and Air Pollutants on COVID-19 Transmission under the Action of Control Measures

Author

Listed:
  • Fei Han

    (School of Information Engineering, China University of Geosciences, Beijing 100083, China)

  • Xinqi Zheng

    (School of Information Engineering, China University of Geosciences, Beijing 100083, China)

  • Peipei Wang

    (School of Information Engineering, China University of Geosciences, Beijing 100083, China)

  • Dongya Liu

    (School of Information Engineering, China University of Geosciences, Beijing 100083, China)

  • Minrui Zheng

    (School of Public Administration and Policy, Renmin University of China, Beijing 100872, China)

Abstract

At present, COVID-19 is still spreading, and its transmission patterns and the main factors that affect transmission behavior still need to be thoroughly explored. To this end, this study collected the cumulative confirmed cases of COVID-19 in China by 8 April 2020. Firstly, the spatial characteristics of the COVID-19 transmission were investigated by the spatial autocorrelation method. Then, the factors affecting the COVID-19 incidence rates were analyzed by the generalized linear mixed effect model (GLMMs) and geographically weighted regression model (GWR). Finally, the geological detector (GeoDetector) was introduced to explore the influence of interactive effects between factors on the COVID-19 incidence rates. The results showed that: (1) COVID-19 had obvious spatial aggregation. (2) The control measures had the largest impact on the COVID-19 incidence rates, which can explain the difference of 34.2% in the COVID-19 incidence rates, while meteorological factors and pollutant factors can only explain the difference of 1% in the COVID-19 incidence rates. It explains that some of the literature overestimates the impact of meteorological factors on the spread of the epidemic. (3) The influence of meteorological factors was stronger than that of air pollution factors, and the interactive effects between factors were stronger than their individual effects. The interaction between relative humidity and NO 2 was stronger. The results of this study will provide a reference for further prevention and control of COVID-19.

Suggested Citation

  • Fei Han & Xinqi Zheng & Peipei Wang & Dongya Liu & Minrui Zheng, 2022. "Effects of Meteorological Factors and Air Pollutants on COVID-19 Transmission under the Action of Control Measures," IJERPH, MDPI, vol. 19(15), pages 1-19, July.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:15:p:9323-:d:876026
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    References listed on IDEAS

    as
    1. A. Stewart Fotheringham & Taylor M. Oshan, 2016. "Geographically weighted regression and multicollinearity: dispelling the myth," Journal of Geographical Systems, Springer, vol. 18(4), pages 303-329, October.
    2. Daniel P. McMillen, 2004. "Geographically Weighted Regression: The Analysis of Spatially Varying Relationships," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 554-556.
    3. Dongya Liu & Xinqi Zheng & Lei Zhang, 2021. "Simulation of Spatiotemporal Relationship between COVID-19 Propagation and Regional Economic Development in China," Land, MDPI, vol. 10(6), pages 1-15, June.
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