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
has 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	MedRxiv
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. We use the model to assess parameter sensitivity for a number of key variables that characterise the COVID-19 epidemic. We also test several control parameters with respect to their influence on the severity of the outbreak. Our analysis shows that due to inclusion of spatial heterogeneity in the population and the asynchronous timing of the epidemic across different areas, the severity of the epidemic might be lower than expected from other models. We find that one of the most crucial control parameters that may significantly reduce the severity of the epidemic is the degree of separation of vulnerable people and people aged 70 years and over, but note also that isolation of other groups has an effect on the severity of the epidemic. It is important to remember that models are there to advise and not to replace reality, and that any action should be coordinated and approved by public health experts with experience in dealing with epidemics. The computational approach makes it possible for further extensive scenario-based analysis to be undertaken. This and a comprehensive study of sensitivity of the model to different parameters defining COVID-19 and its development will be the subject of our forthcoming paper. In that paper, we shall also extend the model where we will consider different probabilistic scenarios for infected people with mild and severe cases.
has issue date	2020-04-07(xsd:dateTime)
bibo:doi	10.1101/2020.04.04.20053462
has license	medrxiv
sha1sum (hex)	f9fcab49021565e1fbb3e6bfb5c3f1477f335a3a
schema:url	https://doi.org/10.1101/2020.04.04.20053462
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:f9fcab49021565e1fbb3e6bfb5c3f1477f335a3a#body_text
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