About: Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China

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About: Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China
Creator	Huang, Qing Sun, Yi Liu, Bang Chen, Kun Yang, Le Wu, Siying Li, Huangyuan Lin, Shaowei Maria, Monica Paoliello, Bastos Wei, Donghong
source	Elsevier; Medline; PMC
abstract	Coronavirus disease 2019 (COVID-19) was first detected in December 2019 in Wuhan, China, with 11,669,259 positive cases and 539,906 deaths globally as of July 8, 2020. The objective of the present study was to determine whether meteorological parameters and air quality affect the transmission of COVID-19, analogous to SARS. We captured data from 29 provinces, including numbers of COVID-19 cases, meteorological parameters, air quality and population flow data, between Jan 21, 2020 and Apr 3, 2020. To evaluate the transmissibility of COVID-19, the basic reproductive ratio (R(0)) was calculated with the maximum likelihood “removal” method, which is based on chain-binomial model, and the association between COVID-19 and air pollutants or meteorological parameters was estimated by correlation analyses. The mean estimated value of R(0) was 1.79 ± 0.31 in 29 provinces, ranging from 1.08 to 2.45. The correlation between R(0) and the mean relative humidity was positive, with coefficient of 0.370. In provinces with high flow, indicators such as carbon monoxide (CO) and 24-h average concentration of carbon monoxide (CO_24 h) were positively correlated with R(0), while nitrogen dioxide (NO(2)), 24-h average concentration of nitrogen dioxide (NO(2)_24 h) and daily maximum temperature were inversely correlated to R(0), with coefficients of 0.644, 0.661, −0.636, −0.657, −0.645, respectively. In provinces with medium flow, only the weather factors were correlated with R(0), including mean/maximum/minimum air pressure and mean wind speed, with coefficients of −0.697, −0.697, −0.697 and −0.841, respectively. There was no correlation with R(0) and meteorological parameters or air pollutants in provinces with low flow. Our findings suggest that higher ambient CO concentration is a risk factor for increased transmissibility of the novel coronavirus, while higher temperature and air pressure, and efficient ventilation reduce its transmissibility. The effect of meteorological parameters and air pollutants varies in different regions, and requires that these issues be considered in future modeling disease transmissibility.
has issue date	2020-08-05(xsd:dateTime)
bibo:doi	10.1016/j.ecoenv.2020.111035
bibo:pmid	32768746
has license	no-cc
sha1sum (hex)	66fd5bc53d0ca3da3625e21391ed694b553c7a86
schema:url	https://doi.org/10.1016/j.ecoenv.2020.111035
resource representing a document's title	Region-specific air pollutants and meteorological parameters influence COVID-19: A study from mainland China
has PubMed Central identifier	PMC7406240
has PubMed identifier	32768746
schema:publication	Ecotoxicol Environ Saf
resource representing a document's body	covid:66fd5bc53d0ca3da3625e21391ed694b553c7a86#body_text
is schema:about of	named entity 'modeling' named entity 'concentration' named entity 'air pollutants' named entity 'maximum' named entity 'correlation' named entity 'correlation' named entity 'meteorological' named entity 'MAINLAND CHINA' named entity 'PARAMETERS' named entity 'higher' named entity 'ratio' named entity 'weather' named entity 'transmissibility' named entity 'carbon monoxide' named entity 'nitrogen dioxide' named entity 'air pressure' named entity 'carbon monoxide' named entity '2.45' named entity 'relative humidity' named entity 'COVID-19' named entity 'nitrogen dioxide' named entity 'air pollutants' named entity 'provinces' named entity 'diurnal temperature range' named entity 'SARS' named entity 'meteorological data' named entity 'carboxyhemoglobin' named entity '1.08' named entity 'COVID' named entity 'Steven Riley' named entity 'COVID' named entity 'respiratory symptoms' named entity '95% confidence interval' named entity 'daily air' named entity 'Coronavirus' named entity 'pollutant' named entity 'IBM SPSS Statistics' named entity 'virus' named entity 'transmissibility' named entity 'covid-19' named entity 'Steven Riley' named entity 'COVID-19' named entity 'air pressure' named entity 'Wuhan' named entity 'kPa' named entity 'air quality' named entity 'Wuhan' named entity 'statistical analyses' named entity 'COVID' named entity 'infection' named entity 'Henan' named entity 'human health' named entity 'air quality' named entity 'Chinese Spring Festival' named entity 'ecological fallacy' named entity 'COVID-19' named entity 'Spearman correlation' named entity 'airway' named entity 'COVID' named entity 'high air pressure' named entity 'COVID' named entity 'COVID' named entity 'transmissibility' named entity 'virus' named entity 'cardiovascular' named entity 'COVID' named entity 'Pearson correlation coefficient' named entity 'Humidity' named entity 'myoglobin' named entity 'virus' named entity 'air quality indices'

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