Journal of Data Science

The new coronavirus disease (COVID-19), as a new infectious disease, has relatively strong ability to spread from person to person. This paper studies several meteorological factors and air quality indicators between Shenzhen and Wenzhou, China, and conducts modelling analysis on whether the transmission of COVID-19 is affected by atmosphere. A comparative assessment is made on the characteristics of meteorological factors and air quality in these two typical cities in China and their impacts on the spread of COVID-19. The article uses meteorological data and air quality data, including 7 variables: daily average temperature, daily average relative humidity, daily average wind speed, nitrogen dioxide (NO2), atmospheric fine particulate matter (PM2.5), carbon monoxide (CO) and ozone (O3), a distributed lag non-linear model (DLNM) is constructed to explore the correlation between atmospheric conditions and non-imported confirmed cases of COVID-19, and the relative risk is introduced to measure the lagging effects of meteorological factors and air pollution on the number of non-imported confirmed cases. Our finding indicates that there is significant differences in the relationship between 7 predictors and the transmission of COVID-19 in Shenzhen and Wenzhou. However, all predictors between the two cities have a non-linear relationship with the number of non-imported confirmed cases. The lower daily average temperature has increased the risk of epidemic transmission in the two cities. As the temperature rises, the risk of epidemic transmission in both cities will significantly decrease. The average daily relative humidity has no significant effects on the epidemic situation in Shenzhen, but the lower relative humidity reduces the risk of epidemic spread in Wenzhou. In contrast, meteorological data have significant impacts on the spread of COVID-19 in Wenzhou. The four predictors (NO2, PM2.5, CO, and O3) have significant effects on the number of nonimported confirmed cases. Among them, PM2.5 has a significant positive correlation with the number of non-imported confirmed cases. Daily average wind speed, NO2 and O3 have different effects on the number of non-imported confirmed cases in different cities.