About: An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China
Creator	Wang, Quanyi Yang, Peng Grenfell, Bryan Yang, Ruifu Dye, Christopher Chen, Bin Tian, Huaiyu Xu, Bo Yang, Qiqi Cui, Yujun Liu, Yonghong Kraemer, Moritz Pybus, Oliver Wu, Chieh-Hsi Cai, Jun Song, Yimeng Wang, Ben Li, Yidan Bjornstad, Ottar Li, Bingying Zheng, Pai
source	Medline; PMC
abstract	Responding to an outbreak of a novel coronavirus (agent of COVID-19) in December 2019, China banned travel to and from Wuhan city on 23 January and implemented a national emergency response. We investigated the spread and control of COVID-19 using a unique data set including case reports, human movement and public health interventions. The Wuhan shutdown was associated with the delayed arrival of COVID-19 in other cities by 2.91 days (95%CI: 2.54-3.29). Cities that implemented control measures pre-emptively reported fewer cases, on average, in the first week of their outbreaks (13.0; 7.1-18.8) compared with cities that started control later (20.6; 14.5-26.8). Suspending intra-city public transport, closing entertainment venues and banning public gatherings were associated with reductions in case incidence. The national emergency response appears to have delayed the growth and limited the size of the COVID-19 epidemic in China, averting hundreds of thousands of cases by 19 February (day 50).
has issue date	2020-03-31(xsd:dateTime)
bibo:doi	10.1126/science.abb6105
bibo:pmid	32234804
has license	cc-by
sha1sum (hex)	7ec7962b9724b94f0085b4974f86575066a0c9df
schema:url	https://doi.org/10.1126/science.abb6105
resource representing a document's title	An investigation of transmission control measures during the first 50 days of the COVID-19 epidemic in China
has PubMed Central identifier	PMC7164389
has PubMed identifier	32234804
schema:publication	Science
resource representing a document's body	covid:7ec7962b9724b94f0085b4974f86575066a0c9df#body_text
is schema:about of	named entity 'control measures' named entity 'COVID-19' named entity 'residuals' named entity 'correlation' named entity 'Table 1' named entity 'public transport' named entity 'data collected' named entity 'Wuhan' named entity 'variance' named entity 'normal distribution' named entity 'alternative hypotheses' named entity 'ordinal variable' named entity 'confidence interval' named entity 'Wuhan' named entity 'Shapiro-Wilk' named entity 'dummy variable' named entity 'COVID-19' named entity 'Wuhan' named entity 'Shanghai' named entity 'residuals' named entity 'linear regression model' named entity 'correlation' named entity '95%' named entity 'Hubei Province' named entity 'Akaike Information Criterion' named entity 'residuals' named entity 'Poisson regression' named entity 'residuals' named entity 'Gaussian' named entity 'regression analysis' named entity 'Wuhan' named entity 'non-parametric' named entity 'heteroscedasticity' named entity 'MCMC' named entity 'COVID-19' named entity 'simplifying assumptions' named entity 'autocorrelation' named entity 'SEIR' named entity 'residuals' named entity 'correlation' named entity 'COVID-19' named entity 'residuals' named entity 'Q-Q' named entity 'regression coefficients' named entity 'transmission rate' named entity 'COVID-19' named entity 'Wuhan' named entity 'regression models' named entity 'residuals' named entity 'pairwise differences' named entity 'MCMC' named entity 'linear regression model' named entity 'model fitting' named entity 'Poisson regression' named entity 'loglinear model' named entity 'Breusch' named entity 'nonlinear' named entity 'Wuhan' named entity 'Chinese New Year' named entity 'Poisson' named entity 'correlation'

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