About: Impact of meteorological factors on the COVID-19 transmission: A multi-city study in China

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About: Impact of meteorological factors on the COVID-19 transmission: A multi-city study in China Goto Sponge NotDistinct Permalink

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Impact of meteorological factors on the COVID-19 transmission: A multi-city study in China
Creator	Zhang, Kai Li, Sheng Wang, Bo Luo, Bin Yan, Jun Fu, Shihua He, Xiaotao Liu, Jiangtao Niu, Jingping Zhang, K Zhou, Ji Zhang, ( Li, Lanyu Niu, Tingting Ren, Xiaowei Shi, Yanjun Xu, Xiaocheng Yao, Jinxi Zhang, Xiuxia Zhu, Weihao
Source	Elsevier; Medline; PMC
abstract	Abstract The purpose of the present study is to explore the associations between novel coronavirus disease 2019 (COVID-19) case counts and meteorological factors in 30 provincial capital cities of China. We compiled a daily dataset including confirmed case counts, ambient temperature (AT), diurnal temperature range (DTR), absolute humidity (AH) and migration scale index (MSI) for each city during the period of January 20th to March 2nd, 2020. First, we explored the associations between COVID-19 confirmed case counts, meteorological factors, and MSI using non-linear regression. Then, we conducted a two-stage analysis for 17 cities with more than 50 confirmed cases. In the first stage, generalized linear models with negative binomial distribution were fitted to estimate city-specific effects of meteorological factors on confirmed case counts. In the second stage, the meta-analysis was conducted to estimate the pooled effects. Our results showed that among 13 cities that have less than 50 confirmed cases, 9 cities locate in the Northern China with average AT below 0 °C, 12 cities had average AH below 4 g/m3, and one city (Haikou) had the highest AH (14.05 g/m3). Those 17 cities with 50 and more cases accounted for 90.6% of all cases in our study. Each 1 °C increase in AT and DTR was related to the decline of daily confirmed case counts, and the corresponding pooled RRs were 0.80 (95% CI: 0.75, 0.85) and 0.90 (95% CI: 0.86, 0.95), respectively. For AH, the association with COVID-19 case counts were statistically significant in lag 07 and lag 014. In addition, we found the all these associations increased with accumulated time duration up to 14 days. In conclusions, meteorological factors play an independent role in the COVID-19 transmission after controlling population migration. Local weather condition with low temperature, mild diurnal temperature range and low humidity likely favor the transmission.
has issue date	2020-07-15(xsd:dateTime)
bibo:doi	10.1016/j.scitotenv.2020.138513
bibo:pmid	32304942
has license	els-covid
sha1sum (hex)	b51e0ba3a7aebd8bf8e18fc0d82384ebd135192a
schema:url	https://doi.org/10.1016/j.scitotenv.2020.138513
resource representing a document's title	Impact of meteorological factors on the COVID-19 transmission: A multi-city study in China
has PubMed Central identifier	PMC7194892
has PubMed identifier	32304942
schema:publication	Science of The Total Environment
resource representing a document's body	covid:b51e0ba3a7aebd8bf8e18fc0d82384ebd135192a#body_text
is schema:about of	named entity 'case' named entity 'addition' named entity 'population' named entity 'condition' named entity 'Each' named entity 'COVID-19' named entity 'meteorological' named entity 'ABSOLUTE HUMIDITY' named entity 'RELATED' named entity 'LIKELY' named entity 'ADDITION' named entity 'POOLED' named entity 'ASSOCIATION' named entity 'CASES' named entity 'LOW HUMIDITY' named entity 'RRS' named entity 'duration' named entity 'weather' named entity 'cities' named entity 'COVID-19' named entity 'case' named entity 'Population' named entity 'play' named entity 'humidity' named entity '95% CI' named entity 'Absolute humidity' named entity 'COVID-19' named entity 'days' named entity 'China' named entity 'China' named entity 'epidemic' named entity 'meta-analysis' named entity 'DTR' named entity 'infectious diseases' named entity 'COVID-19' named entity 'COVID-19' named entity 'Wuhan' named entity 'COVID-19' named entity 'dengue virus' named entity 'COVID-19' named entity 'Hubei' named entity 'COVID-19' named entity 'public health control' named entity 'non-linear' named entity 'China' named entity 'STATA' named entity 'humidity' named entity 'DTR' named entity 'Nanchang' named entity 'China' named entity 'low humidity' named entity 'DTR' named entity 'cubic splines' named entity 'influenza virus' named entity 'doubling time' named entity 'epidemic' named entity 'confidence intervals' named entity 'COVID-19' named entity 'China' named entity 'diurnal temperature range' named entity 'non-linear regression' named entity 'China'

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