








key: cord-301491-frv4c5ny
authors: Ran, Jinjun; Zhao, Shi; Han, Lefei; Chen, Dieyi; Yang, Zuyao; Yang, Lin; Wang, Maggie H; He, Daihai
title: The Ambient Ozone and COVID-19 Transmissibility in China: A Data-Driven Ecological Study of 154 Cities
date: 2020-07-08
journal: J Infect
DOI: 10.1016/j.jinf.2020.07.011
sha: 
doc_id: 301491
cord_uid: frv4c5ny

• We quantified the COVID-19 transmissibility by the basic reproductive number. • The COVID-19 transmissibility could be negatively associated with ambient ozone. • The daily 1-h maximum ozone might cover 7.6% of the transmissibility variability. • It echoes a previous study of negative effects of ozone on the flu transmission. • The ground-level ozone may be a “double-edged sword” to public health.

The coronavirus disease 2019 (COVID-19) has triggered a pandemic and is still spreading around the world. Exploring the environmental factors that associate with the prevalence and transmission would improve the understanding of COVID-19 and contribute to the long-term control strategies of the outbreak. 1,2 Besides the meteorological factors 3 , to what extend do common air pollutants may affect the COVID-19 transmission remains unclear. To fill this knowledge gap, we examined the associations between common air pollutants and COVID-19 transmissibility in Chinese cities.

We obtained the daily count of COVID-19 cases for each Chinese city from the Chinese provincial health agencies and China National Health Commission (CNHC). 4 We quantified the transmissibility of COVID-19 by using the basic reproduction number (R 0 ), a unit-free measure of infectivity that is commonly used in infectious disease epidemiology. Then, we filtered out a case series of each city from the first-case occurrence to the following 16 days for estimating the R 0. 5 We calculated the R 0 for each Chinese city with a Gamma distribution having mean (±SD) values of 5.5 (±3.3) days for the serial interval averaged from previous estimations. The calculation in detail was shown in supplementary materials. As R 0 is a measurement regarding the population as a whole, the demographic heterogeneities across each city could be thus neglected.

We obtained air pollutants monitoring data in 1,642 stations from China National Environmental Center between December 10, 2019 and February 29, 2020, including ozone (O 3 ) with three metrics daily average, daily 1-h maximum, and daily 8-h maximum, as well as other criteria pollutants.

After calculating their average values across the time period, we computed a raster for each pollutant by the kriging interpolation. We then extracted and linked pollutants' values to each Chinese city.

The spatial distribution of O 3 (1-h maximum) was shown in Fig. 1A . The corresponding meteorological factors were addressed and matched by the same method. We adopted the nonlinear (i) univariable, and (ii) multivariable, i.e., adjusted by temperature and absolute humidity, regression analyses to explore the association between R 0 and each pollutant. We carried out the fitting procedure considering two weighting schemes that are (i) equal-weighted, and (ii) weighted by the total number of cases from each city. In addition, we employed the spline regression and permutation and perturbation analysis as sensitivity analysis for validation.

A total of 154 Chinese cities were detected with the COVID-19 outbreak (R 0 > 1) and were included in the regression analysis. The maximal R 0 was estimated at 2.5 (95%CI: 2.4−2.6) in Wuhan (Table   1) , which is largely consistent with previous estimates. 6, 7 Their spatial distribution was shown in Fig.   1B . We found that the R 0 of COVID-19 was negatively associated with the daily 1-h maximum O 3 concentration for both univariable and multivariable analyses and both weighting schemes significantly and consistently ( Fig. 1C and Fig. S1 ). By contrast, associations with other pollutants failed to reach statistical significance. The average concentration of daily 1-h maximum O 3 across the 154 cities has a median at 73.1 µg/m 3 and ranges from 51.6 µg/m 3 to 106.7 µg/m 3 (Table 1) . The

Spearman's ranked correlation coefficient between daily 1-h maximum O 3 and R 0 was −0.21 (95% CI: −0.33, −0.02) (Table S1 ). We estimated that the ambient O 3 could solely explain 7.6% of the variability in the R 0 of COVID-19 in terms of McFadden's pseudo-R-squared for the univariable analysis, and this term increased to 9.3% in the multivariable analysis. A similar protective association between O 3 and the transmissibility of influenza was found in a previous study. 8 The spline regression validated the negative association (Fig. S2) , and the permutation and perturbation tests represented that the observed relationship was unlikely due to chance.

To our best knowledge, this is the first study on the association of COVID-19 transmissibility with ambient ozone. Ambient ozone was associated in reducing COVID-19 transmissibility probably due to its virucidal activity and possible impact on host defense. Ozone gas is highly effective in broadspectrum disinfection and sterilization against many repertory infections, such as SARS and influenza viruses. 9 Ozone-primed immunity against viral infection might also play a critical role in the reduction of COVID-19 infectivity. Specifically, exposure to ambient ozone may trigger slight allergic reactions to human, which may enhance pulmonary innate immunity. Two significances of the current study were emphasized here. First, as the weather getting warmer in the Northern hemisphere, the ambient ozone concentration in many places will gradually escalate, and this could be a good sign for the COVID-19 control regionally. Second, as a highly reactive oxidant air composition, ground-level ozone is also a "double-edged sword" to public health. We advocate that more attention on the good side of ambient ozone and the trade-off of ambient ozone control to avoid over-vigilance. 

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The role of the environment and its pollution in the prevalence of COVID-19

No Association of COVID-19 transmission with temperature or UV radiation in Chinese cities

Open-source analytics tools for studying the COVID-19 coronavirus outbreak

The role of absolute humidity on transmission rates of the COVID-19 outbreak

Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak

Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus-Infected Pneumonia

Ambient ozone and influenza transmissibility in Hong Kong

IL-33 isoforms: Their future as vaccine adjuvants?

IL-33 drives augmented responses to ozone in obese mice

Dynamical resonance can account for seasonality of influenza epidemics

2020 by spatial interpolation with inverse distance weighting. (B) Spatial distribution of basic reproductive number (R 0 ) across 154 Chinese cities. (C) Estimated nonlinear relationships and corresponding confidence intervals between the R 0 and daily 1-h maximum ozone concentrations in the multivariable regression analysis with two weighting schemes (equal-weighted and weighted by the number of cases). The violin plot on the left of the panel c indicates the distribution of R 0 and the plot in the bottom shows the distribution of ambient 1-h maximum ozone concentrations



