About: A mechanism-based parameterisation scheme to investigate the association between transmission rate of COVID-19 and meteorological factors on plains in China

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About: A mechanism-based parameterisation scheme to investigate the association between transmission rate of COVID-19 and meteorological factors on plains in China Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.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	A mechanism-based parameterisation scheme to investigate the association between transmission rate of COVID-19 and meteorological factors on plains in China
Creator	Chan, Jimmy Fung, Jimmy Lau, Alexis Lin, Changqing Bo, Yacong Guo, Cui Hossain, Shakhaoat Lao, Xiang, Qian Yeung, David Zeng, Yiqian Zhang, Yumiao
source	Elsevier; Medline; PMC
abstract	The novel coronavirus disease 2019 (COVID-19), which first emerged in Hubei province, China, has become a pandemic. However, data regarding the effects of meteorological factors on its transmission are limited and inconsistent. A mechanism-based parameterisation scheme was developed to investigate the association between the scaled transmission rate (STR) of COVID-19 and the meteorological parameters in 20 provinces/municipalities located on the plains in China. We obtained information on the scale of population migrated from Wuhan, the world epicentre of the COVID-19 outbreak, into the study provinces/municipalities using mobile-phone positioning system and big data techniques. The highest STRs were found in densely populated metropolitan areas and in cold provinces located in north-eastern China. Population density had a non-linear relationship with disease spread (linearity index, 0.9). Among various meteorological factors, only temperature was significantly associated with the STR after controlling for the effect of population density. A negative and exponential relationship was identified between the transmission rate and the temperature (correlation coefficient, −0.56; 99% confidence level). The STR increased substantially as the temperature in north-eastern China decreased below 0 °C (the STR ranged from 3.5 to 12.3 when the temperature was between −9.41 °C and −13.87 °C), whilst the STR showed less temperature dependence in the study areas with temperate weather conditions (the STR was 1.21 ± 0.57 when the temperature was above 0 °C). Therefore, a higher population density was linearly whereas a lower temperature (<0 °C) was exponentially associated with an increased transmission rate of COVID-19. These findings suggest that the mitigation of COVID-19 spread in densely populated and/or cold regions will be a great challenge.
has issue date	2020-10-01(xsd:dateTime)
bibo:doi	10.1016/j.scitotenv.2020.140348
bibo:pmid	32569904
has license	no-cc
sha1sum (hex)	007e96d22dc1a26a3ca72074945f1b1242ceef1a
schema:url	https://doi.org/10.1016/j.scitotenv.2020.140348
resource representing a document's title	A mechanism-based parameterisation scheme to investigate the association between transmission rate of COVID-19 and meteorological factors on plains in China
has PubMed Central identifier	PMC7301117
has PubMed identifier	32569904
schema:publication	Sci Total Environ
resource representing a document's body	covid:007e96d22dc1a26a3ca72074945f1b1242ceef1a#body_text
is schema:about of	named entity 'higher' named entity 'COVID-19' named entity 'CONDITIONS' named entity 'TEMPERATURE DEPENDENCE' named entity 'DECREASED' named entity 'STR' named entity 'STR' named entity 'Population density' named entity 'lower temperature' named entity 'COVID-19' named entity 'COVID-19' named entity 'temperature' named entity 'health systems' named entity 'Heilongjiang' named entity 'Chinese New Year' named entity 'transmission rate' named entity 'severe acute respiratory syndrome' named entity 'relative humidity' named entity 'initial infection' named entity '95% CI' named entity 'correlation coefficient' named entity 'China' named entity 'Johns Hopkins University' named entity 'Guangxi' named entity 'north-eastern China' named entity 'COVID' named entity 'Shaanxi' named entity 'COVID' named entity 'Anhui' named entity 'Jiangxi' named entity 'disease transmission' named entity 'population density' named entity 'DWY' named entity 'COVID' named entity 'statistical models' named entity 'Spain' named entity 'COVID-19' named entity 'COVID' named entity 'Indonesia' named entity 'transmission rate' named entity 'COVID' named entity 'COVID' named entity 'COVID' named entity 'Hong Kong' named entity 'population density' named entity 'COVID-19 outbreak' named entity 'Hubei' named entity 'Tianjin' named entity 'transmission rate' named entity 'Macau' named entity 'transmission rate' named entity 'World Meteorological Organization' named entity '0.9' named entity 'correlation coefficient' named entity 'disease transmission' named entity 'north-eastern China' named entity 'viruses' named entity 'COVID' named entity 'Population density' named entity 'China' named entity 'Wuhan' named entity 'epicentre' named entity 'Jiangsu' named entity 'China' named entity 'COVID-19' named entity 'population density' named entity 'Heilongjiang' named entity 'northeastern China' named entity 'COVID' named entity 'positive correlation'

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