About: Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example

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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	Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example
Creator	Ahmad, Shazaad House, Thomas Dark, Paul Felton, Timothy Pellis, Lorenzo Ustianowski, Andrew Gent, Nick Hall, Ian Hall, Thomas Curran-Sebastian, Jacob Das, Rajenki Fearon, Elizabeth Fyles, Martyn Lewkowicz, Hugo Overton, Christopher Pang, Xiaoxi Stage, Helena Vekaria, Bindu Webb, Luke Ahmad, Jacob Curran-Sebastian, Paul Dark, Rajenki Das, Elizabeth Fearon, Timothy Felton, Martyn Fyles, Nick Gent, Ian House, Hugo Lewkowicz, Xiaoxi Overton, Christophere Pang, Lorenzo Pellis, Robert Sawko, Andrew Sawko, Robert Stage, Shazaad Ustianowski, Bindu Vekaria, Luke Webb,
source	Elsevier; Medline; PMC
abstract	During an infectious disease outbreak, biases in the data and complexities of the underlying dynamics pose significant challenges in mathematically modelling the outbreak and designing policy. Motivated by the ongoing response to COVID-19, we provide a toolkit of statistical and mathematical models beyond the simple SIR-type differential equation models for analysing the early stages of an outbreak and assessing interventions. In particular, we focus on parameter estimation in the presence of known biases in the data, and the effect of non-pharmaceutical interventions in enclosed subpopulations, such as households and care homes. We illustrate these methods by applying them to the COVID-19 pandemic.
has issue date	2020-07-04(xsd:dateTime)
bibo:doi	10.1016/j.idm.2020.06.008
bibo:pmid	32691015
has license	no-cc
sha1sum (hex)	b9704e32040a1020ad8701ee03df56825d2e5f77
schema:url	https://doi.org/10.1016/j.idm.2020.06.008
resource representing a document's title	Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example
has PubMed Central identifier	PMC7334973
has PubMed identifier	32691015
schema:publication	Infect Dis Model
resource representing a document's body	covid:b9704e32040a1020ad8701ee03df56825d2e5f77#body_text
is schema:about of	named entity 'Journal' named entity 'early stages' named entity 'complexities' named entity 'assessing' named entity 'COVID-19' named entity 'During' named entity 'differential equation' named entity 'parameter estimation' named entity 'infectious disease' named entity 'analysing' named entity 'care homes' named entity 'prodromal' named entity 'epidemic' named entity 'pathogens' named entity 'deterministic' named entity 'statistical methods' named entity 'epidemic' named entity 'epidemic' named entity 'contact tracing' named entity 'epidemic' named entity 'incubation period' named entity 'SEIR' named entity 'epidemic' named entity 'care homes' named entity 'epidemic' named entity 'random process' named entity 'epidemic' named entity 'achieve control' named entity 'MERS' named entity 'care home' named entity 'infection' named entity '2009 H1N1 pandemic' named entity 'infection' named entity 'branching process' named entity 'household model' named entity 'care homes' named entity 'coronavirus' named entity 'COVID' named entity 'infection' named entity 'symptom' named entity 'infection' named entity 'discrete time' named entity 'infection' named entity 'epidemic'

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