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

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

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
has 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 Curran-Sebastian, Jacob Das, Rajenki Fearon, Elizabeth Fyles, Martyn Lewkowicz, Hugo Overton, Christopher Pang, Xiaoxi Stage, Helena Vekaria, Bindu Webb, Luke Sawko, Robert
Source	ArXiv
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	2020-05-11(xsd:dateTime)
has license	arxiv
sha1sum (hex)	023f34678b688f43348eabe8a71364850d3996b3
resource representing a document's title	Using statistics and mathematical modelling to understand infectious disease outbreaks: COVID-19 as an example
resource representing a document's body	covid:023f34678b688f43348eabe8a71364850d3996b3#body_text
is schema:about of	named entity 'MATHEMATICAL MODELS' named entity 'DYNAMICS' named entity 'EARLY STAGES' named entity 'SIGNIFICANT' named entity 'STATISTICAL' named entity 'KNOWN' named entity 'NON-' named entity 'DATA' named entity 'infectious disease' named entity 'COVID-19 pandemic' named entity 'infectious disease outbreaks' named entity 'mathematical modelling' named entity 'growth rate' named entity 'infection' named entity 'control policies' named entity 'quarantine' named entity 'Wuhan' named entity 'grow exponentially' named entity 'Spring Festival' named entity 'epidemic' named entity 'doubling time' named entity 'decision making' named entity 'force of infection' named entity 'MERS outbreaks' named entity 'quarantine' named entity 'immunocompromised' named entity 'epidemic' named entity 'prodromal' named entity 'control policies' named entity 'prodromal' named entity 'epidemic' named entity 'care homes' named entity 'Geometric distribution' named entity 'quarantine' named entity 'epidemic' named entity 'epidemic' named entity 'epidemiological' named entity 'long term' named entity 'probability' named entity 'stochastic' named entity 'infectious disease outbreaks' named entity 'care homes' named entity 'epidemic' named entity 'household model' named entity 'pathogen' named entity 'vaccination' named entity 'SARS-CoV2' named entity 'vaccination' named entity 'reproduction number' named entity 'quarantine' named entity 'quarantine' named entity 'generation time' named entity '2009 H1N1 pandemic' named entity 'quarantine' named entity 'doubling time' named entity 'malaria' named entity 'prodromal' named entity 'COVID' named entity 'China' named entity 'epidemic model' named entity 'growth rate' named entity 'worst case scenario' named entity 'incubation period' named entity 'MERS' named entity 'care home' named entity 'pre-symptomatic' named entity 'contact traced' named entity 'contact tracing' named entity 'coronaviruses'

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