About: Generic probabilistic modelling and non-homogeneity issues for the UK epidemic of COVID-19

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About: Generic probabilistic modelling and non-homogeneity issues for the UK epidemic of COVID-19 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
title	Generic probabilistic modelling and non-homogeneity issues for the UK epidemic of COVID-19
Creator	Crick, Tom De Arruda, Edilson Fedorov, Val Fesenko, Ivan Gartner, Daniel Gillard, Jonathan Grimsley, Jasmine Harper, Paul Kremnizer, Kobi Noonan, Jack Whitaker, Roger Woolley, Thomas Zhigljavsky, Anatoly
source	ArXiv
abstract	Coronavirus COVID-19 spreads through the population mostly based on social contact. To gauge the potential for widespread contagion, to cope with associated uncertainty and to inform its mitigation, more accurate and robust modelling is centrally important for policy making. We provide a flexible modelling approach that increases the accuracy with which insights can be made. We use this to analyse different scenarios relevant to the COVID-19 situation in the UK. We present a stochastic model that captures the inherently probabilistic nature of contagion between population members. The computational nature of our model means that spatial constraints (e.g., communities and regions), the susceptibility of different age groups and other factors such as medical pre-histories can be incorporated with ease. We analyse different possible scenarios of the COVID-19 situation in the UK. Our model is robust to small changes in the parameters and is flexible in being able to deal with different scenarios. This approach goes beyond the convention of representing the spread of an epidemic through a fixed cycle of susceptibility, infection and recovery (SIR). It is important to emphasise that standard SIR-type models, unlike our model, are not flexible enough and are also not stochastic and hence should be used with extreme caution. Our model allows both heterogeneity and inherent uncertainty to be incorporated. Due to the scarcity of verified data, we draw insights by calibrating our model using parameters from other relevant sources, including agreement on average (mean field) with parameters in SIR-based models.
has issue date	2020-04-04(xsd:dateTime)
has license	arxiv
sha1sum (hex)	0f23d5716eca022036940aca5ca7eabcb2117f6b
resource representing a document's title	Generic probabilistic modelling and non-homogeneity issues for the UK epidemic of COVID-19
resource representing a document's body	covid:0f23d5716eca022036940aca5ca7eabcb2117f6b#body_text
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