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

Int J Environ Res Public Health. 2022 Jul 29;19(15):9323. doi: 10.3390/ijerph19159323.

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 NO2 was stronger. The results of this study will provide a reference for further prevention and control of COVID-19.

Keywords: COVID-19 incidence rates; GWR; GeoDetector; interaction; spatial autocorrelation.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Air Pollutants* / analysis
  • Air Pollution* / analysis
  • COVID-19* / epidemiology
  • China / epidemiology
  • Humans
  • Meteorological Concepts
  • Particulate Matter / analysis
  • Spatial Regression

Substances

  • Air Pollutants
  • Particulate Matter

Grants and funding

This research was supported by the Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources (No. KF-2020-05-063), the Fundamental Research Funds for the Central Universities (No. 2652019001, No. 2652020004, No. 2652020002), and 2021 Graduate Innovation Fund Project of China University of Geosciences, Beijing (No. ZY2021YC010).