About: Modelling insights into the COVID-19 pandemic

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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	Modelling insights into the COVID-19 pandemic
Creator	Adegboye, Oyelola Mcbryde, Emma Meehan, Michael Mcbryde, E Adekunle, Adeshina Trauer, James Caldwell, Jamie Rojas, Diana Turek, Evelyn Williams, Bridget Adegboye, O Adekunle, A Caldwell, J Meehan, M Rojas, D Trauer, J Turek, E Williams, B
source	Elsevier; Medline; PMC
abstract	Coronavirus disease 2019 (COVID-19) is a newly emerged infectious disease caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) that was declared a pandemic by the World Health Organization on 11th March, 2020. Response to this pandemic has required extensive collaboration across the scientific community in an attempt to contain the virus and limit further transmission. Mathematical modelling has been at the forefront of these response efforts by: (1) providing initial estimates of the SARS-CoV-2 reproduction rate, R(0) (of approximately 2-3); (2) updating these estimates following intervention implementation (with significantly reduced, often sub-critical, transmission rates); (3) assessing the potential for global spread through predictions of the exportation of COVID-19 before significant case numbers had been reported internationally; and (4) quantifying the severity and burden of COVID-19, indicating that the true infection rates are orders of magnitude greater than estimates based on confirmed case counts alone. In this review, we highlight the critical role played by mathematical modelling to understand COVID-19 thus far, the challenges posed by data availability and uncertainty, and the continuing utility of modelling-based approaches to inform the public health response. †Unless otherwise stated, all bracketed error margins correspond to the 95% credible interval (CrI) for reported estimates.
has issue date	2020-06-20(xsd:dateTime)
bibo:doi	10.1016/j.prrv.2020.06.014
bibo:pmid	32680824
has license	no-cc
sha1sum (hex)	f9f4cf14d2ade1977d92dbbc3c94165791ba1a77
schema:url	https://doi.org/10.1016/j.prrv.2020.06.014
resource representing a document's title	Modelling insights into the COVID-19 pandemic
has PubMed Central identifier	PMC7305515
has PubMed identifier	32680824
schema:publication	Paediatr Respir Rev
resource representing a document's body	covid:f9f4cf14d2ade1977d92dbbc3c94165791ba1a77#body_text
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