Pub. online:4 Aug 2022Type:Research ArticleOpen Access
Journal:Journal of Data Science
Volume 18, Issue 5 (2020): Special Issue S1 in Chinese (with abstract in English), pp. 889–906
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.
The United States has the highest numbers of confirmed cases of COVID-19 in the world. The early hot spot states were New York, New Jersey, and Connecticut. The workforce in these states was required to work from home except for essential services. It was necessary to evaluate an appropriate date for resumption of business since the premature reopening of the economy would lead to a broader spread of COVID-19, while the opposite situation would cause greater loss of economy. To reflect the real-time risk of the spread of COVID-19, it was crucial to evaluate the population of infected individuals before or never being confirmed due to the pre-symptomatic and asymptomatic transmissions of COVID-19. To this end, we proposed an epidemic model and applied it to evaluate the real-time risk of epidemic for the states of New York, New Jersey, and Connecticut. We used California as the benchmark state because California began a phased reopening on May 8, 2020. The dates on which the estimated numbers of unidentified infectious individuals per 100,000 for states of New York, New Jersey, and Connecticut were close to those in California on May 8, 2020, were June 1, 22, and 22, 2020, respectively. By the practice in California, New York, New Jersey, and Connecticut might consider reopening their business. Meanwhile, according to our simulation models, to prevent resurgence of infections after reopening the economy, it would be crucial to maintain sufficient measures to limit the social distance after the resumption of businesses. This precaution turned out to be critical as the situation in California quickly deteriorated after our analysis was completed and its interventions after the reopening of business were not as effective as those in New York, New Jersey, and Connecticut.