Outdoor Air Pollutant Concentration and COVID-19 Infection in Wuhan, China

Han, Y.; Lam, J. C. K.; Li, V. O. K.; Guo, P.; Zhang, Q.; Wang, A.; Crowcroft, J.; Gozes, I.; Fu, J.; Gilani, Z.; Wang, S.

COVID-19 infection, first reported in Wuhan, China in December 2019, has become a global pandemic, causing significantly high infections and mortalities in Italy, the UK, the US, and other parts of the world. Based on the statistics reported by John Hopkins University, 4.7M people worldwide and 84,054 people in China have been confirmed positive and infected with COVID-19, as of 18 May 2020. Motivated by the previous studies which show that the exposures to air pollutants may increase the risk of influenza infection, our study examines if such exposures will also affect Covid-19 infection. To the best of our understanding, we are the first group in the world to rigorously explore the effects of outdoor air pollutant concentrations, meteorological conditions and their interactions, and lockdown interventions, on Covid-19 infection in China. Since the number of confirmed cases is likely to be under-reported due to the lack of testing capacity, the change in confirmed case definition, and the undiscovered and unreported asymptotic cases, we use the rate of change in the daily number of confirmed infection cases instead as our dependent variable. Even if the number of reported infections is under-reported, the rate of change will still accurately reflect the relative change in infection, provided that the trend of under-reporting remains the same. In addition, the rate of change in daily infection cases can be distorted by the government imposed public health interventions, including the lockdown policy, inter-city and intra-city mobility, and the change in testing capacity and case definition. Hence, the effects of the lockdown policy and the inter-city and intra-city mobility, and the change in testing capacity and case definition are all taken into account in our statistical modelling. Furthermore, we adopt the generalized linear regression models covering both the Negative Binomial Regression and the Poisson Regression. These two regression models, when combined with different time-lags (to reflect the COVID-19 incubation period and delay due to official confirmation) in air pollutant exposure (PM2.5), are used to fit the COVID-19 infection model. Our statistical study has shown that higher PM2.5 concentration is significantly correlated with a higher rate of change in the daily number of confirmed infection cases in Wuhan, China (p < 0.05). We also determine that a higher dew point interacting with a higher PM2.5 concentration is correlated with a higher rate of change in the daily number of confirmed infection cases, while a higher UV index and a higher PM2.5 concentration are correlated with a lower rate of change. Furthermore, we find that PM2.5 concentration eight days ago has the strongest predictive power for COVID-19 Infection. Our study bears significance to the understanding of the effect of air pollutant (PM2.5) on COVID-19 infection, the interaction effects of both the air pollutant concentration (PM2.5) and the meteorological conditions on the rate of change in infection, as well as the insights into whether lockdown should have an effect on COVID-19 infection.