Abstract
Objectives: To evaluate the long- and short-term effects of air pollution on COVID-19 transmission simultaneously, especially in high air pollution level countries. Methods: Quasi-Poisson regression was applied to estimate the association between exposure to air pollution and daily new confirmed cases of COVID-19, with mutual adjustment for long- and short-term air quality index (AQI). The independent effects were also estimated and compared. We further assessed the modification effect of within-city migration (WM) index to the associations. Results: We found a significant 1.61% (95%CI: 0.51%, 2.72%) and 0.35% (95%CI: 0.24%, 0.46%) increase in daily confirmed cases per 1 unit increase in long- and short-term AQI. Higher estimates were observed for long-term impact. The stratifying result showed that the association was significant when the within-city migration index was low. A 1.25% (95%CI: 0.0.04%, 2.47%) and 0.41% (95%CI: 0.30%, 0.52%) increase for long- and short-term effect respectively in low within-city migration index was observed. Conclusions: There existed positive associations between long- and short-term AQI and COVID-19 transmission, and within-city migration index modified the association. Our findings will be of strategic significance for long-run COVID-19 control.
Highlights
COVID-19, firstly emerging in Wuhan, China has become the focus of global public attention [1, 2]
PM2.5, PM10 and O3 were responsible for Air quality index (AQI)
Both short- and long-term AQI were higher in northern China and lower in southern area
Summary
COVID-19, firstly emerging in Wuhan, China has become the focus of global public attention [1, 2]. The epidemic scale of COVID-19 has increased with a rapid increase with confirmed cases increasing across China and worldwide [3]. A mandatory lockdown policy initially launched in Wuhan on January 23 and was followed shortly afterwards by another 95 cities [4]. Local government had utilized restriction measures, the coronavirus still spread widely across China. Previous studies have concluded that the spatial distribution of coronavirus infection may not be explained through the epidemic model, geographical distance, population distribution, and age composition [5,6,7,8]. It will be of great significance to clarify the possible influencing factors such as atmospheric conditions for the COVID-19 epidemic, considering what should be done further to control COVID-19 in the long run [9, 10]
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