posted on 2021-09-24, 17:38authored byQiao Kang, Xing Song, Xiaying Xin, Bing Chen, Yuanzhu Chen, Xudong Ye, Baiyu Zhang
Links between environmental
conditions (e.g., meteorological factors
and air quality) and COVID-19 severity have been reported worldwide.
However, the existing frameworks of data analysis are insufficient
or inefficient to investigate the potential causality behind the associations
involving multidimensional factors and complicated interrelationships.
Thus, a causal inference framework equipped with the structural causal
model aided by machine learning methods was proposed and applied to
examine the potential causal relationships between COVID-19 severity
and 10 environmental factors (NO2, O3, PM2.5,
PM10, SO2, CO, average air temperature, atmospheric pressure,
relative humidity, and wind speed) in 166 Chinese cities. The cities
were grouped into three clusters based on the socio-economic features.
Time-series data from these cities in each cluster were analyzed in
different pandemic phases. The robustness check refuted most potential
causal relationships’ estimations (89 out of 90). Only one
potential relationship about air temperature passed the final test
with a causal effect of 0.041 under a specific cluster-phase condition.
The results indicate that the environmental factors are unlikely to
cause noticeable aggravation of the COVID-19 pandemic. This study
also demonstrated the high value and potential of the proposed method
in investigating causal problems with observational data in environmental
or other fields.