Abstract

This study investigates thoroughly whether acute exposure to outdoor PM2.5 concentration, P, modifies the rate of change in the daily number of COVID-19 infections (R) across 18 high infection provincial capitals in China, including Wuhan. A best-fit multiple linear regression model was constructed to model the relationship between P and R, from 1 January to 20 March 2020, after accounting for meteorology, net move-in mobility (NM), time trend (T), co-morbidity (CM), and the time-lag effects. Regression analysis shows that P (β = 0.4309, p < 0.001) is the most significant determinant of R. In addition, T (β = −0.3870, p < 0.001), absolute humidity (AH) (β = 0.2476, p = 0.002), P × AH (β = −0.2237, p < 0.001), and NM (β = 0.1383, p = 0.003) are more significant determinants of R, as compared to GDP per capita (β = 0.1115, p = 0.015) and CM (Asthma) (β = 0.1273, p = 0.005). A matching technique was adopted to demonstrate a possible causal relationship between P and R across 18 provincial capital cities. A 10 µg/m3 increase in P gives a 1.5% increase in R (p < 0.001). Interaction analysis also reveals that P × AH and R are negatively correlated (β = −0.2237, p < 0.001). Given that P exacerbates R, we recommend the installation of air purifiers and improved air ventilation to reduce the effect of P on R. Given the increasing observation that COVID-19 is airborne, measures that reduce P, plus mandatory masking that reduces the risks of COVID-19 associated with viral-particulate transmission, are strongly recommended. Our study is distinguished by the focus on the rate of change instead of the individual cases of COVID-19 when modelling the statistical relationship between R and P in China; causal instead of correlation analysis via the matching analysis, while taking into account the key confounders, and the individual plus the interaction effects of P and AH on R.

Details

Title
Outdoor PM2.5 concentration and rate of change in COVID-19 infection in provincial capital cities in China
Author
Yang, Han 1 ; Lam Jacqueline C K 1 ; Li Victor O K 1 ; Crowcroft, Jon 2 ; Fu Jinqi 3 ; Downey, Jocelyn 1 ; Gozes Illana 4 ; Zhang, Qi 1 ; Wang, Shanshan 1 ; Gilani Zafar 1 

 The University of Hong Kong, Department of Electrical and Electronic Engineering, Pok Fu Lam, Hong Kong (GRID:grid.194645.b) (ISNI:0000000121742757) 
 The University of Cambridge, Department of Computer Science and Technology, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000000121885934) 
 The University of Cambridge, MRC Cancer Unit, Department of Oncology, Cambridge, UK (GRID:grid.5335.0) (ISNI:0000000121885934) 
 Tel Aviv University, Department of Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine, Adams Super Center for Brain Studies and Sagol School of Neuroscience, Tel Aviv, Israel (GRID:grid.12136.37) (ISNI:0000 0004 1937 0546) 
Publication year
2021
Publication date
2021
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2604980990
Copyright
© The Author(s) 2021. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.