Limited role for meteorological factors on the variability in COVID-19 incidence: A retrospective study of 102 Chinese cities

• Published in: PLoS neglected tropical diseases 2021-02, Vol.15 (2), p.e0009056-e0009056
• Main Authors: Chong, Ka Chun, Ran, Jinjun, Lau, Steven Yuk Fai, Goggins, William Bernard, Zhao, Shi, Wang, Pin, Tian, Linwei, Wang, Maggie Haitian, Mohammad, Kirran N, Wei, Lai, Xiong, Xi, Liu, Hengyan, Chan, Paul Kay Sheung, Wang, Huwen, Wang, Yawen, Wang, Jingxuan
• Format: Article
• Language: English
• Subjects: Biology and Life Sciences; China; China - epidemiology; COVID-19 - epidemiology; Demographic aspects; Earth Sciences; Environment; Environmental aspects; Epidemics; Health risk assessment; Humans; Incidence; Infection control; Infection Control - methods; Medical climatology; Medical research; Medicine and Health Sciences; Medicine, Experimental; Meteorological Concepts; Physical Sciences; Retrospective Studies; SARS-CoV-2 - isolation & purification; Social Sciences
• ISSN: 1935-2735; 1935-2727; 1935-2735
• DOI: 10.1371/journal.pntd.0009056

While many studies have focused on identifying the association between meteorological factors and the activity of COVID-19, we argue that the contribution of meteorological factors to a reduction of the risk of COVID-19 was minimal when the effects of control measures were taken into account. In this study, we assessed how much variability in COVID-19 activity is attributable to city-level socio-demographic characteristics, meteorological factors, and the control measures imposed. We obtained the daily incidence of COVID-19, city-level characteristics, and meteorological data from a total of 102 cities situated in 27 provinces/municipalities outside Hubei province in China from 1 January 2020 to 8 March 2020, which largely covers almost the first wave of the epidemic. Generalized linear mixed effect models were employed to examine the variance in the incidence of COVID-19 explained by different combinations of variables. According to the results, including the control measure effects in a model substantially raised the explained variance to 45%, which increased by >40% compared to the null model that did not include any covariates. On top of that, including temperature and relative humidity in the model could only result in < 1% increase in the explained variance even though the meteorological factors showed a statistically significant association with the incidence rate of COVID-19. In conclusion, we showed that very limited variability of the COVID-19 incidence was attributable to meteorological factors. Instead, the control measures could explain a larger proportion of variance.