Meteorological factors and domestic new cases of coronavirus disease (COVID-19) in nine Asian cities: A time-series analysis

Zonglin He; Yiqiao Chin; Jian Huang; Yi He; Babatunde O. Akinwunmi; Shinning Yu; Casper J.P. Zhang; Wai-kit Ming

This is the first study to statistically analyze the relationship between meteorological factors and the daily new cases of COVID-19. Because of the non-linear nature of the data, we also performed GAMs to quantitate and visualize the relationship using splines functions. However, there are several limitations. Firstly, numbers of cases in Malaysia, Korea, and Japan were obtained from the epidemiologic reports released by the Department of Health in the corresponding countries, instead of a daily update of case numbers, which is not available for these countries. Hence, some cases might be missing. Secondly, the duration of the study period was short and the number of cases in Malaysia, Singapore and Japan were small. Thirdly, we only considered two meteorological factors (temperature and humidity) in this study. Other covariates such as wind speed, pollutant concentration, population density, air conditioning use, rainfall, which could also influence the spread of COVID-19, were not included. Moreover, while we collected the meteorological data from the capital cities of Malaysia, Japan, and Korea, the number of domestic cases at the national level in these three countries was used for analysis due to lack of available data at their city level. Most importantly, we did not incorporate the public health measures into the modelling, which may greatly confound the results, but we chose the date when accumulated confirmed cases exceeding 30, based on the postulation that a certain level of public health measures had been carried out at that time, thus the confounding effects can be mitigated at some point.