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

Generic probabilistic modelling and non-homogeneity issues for the UK epidemic of COVID-19
Generic probabilistic modelling and non-homogeneity issues for the UK epidemic of COVID-19
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.
stat.AP, physics.soc-ph, q-bio.PE
Zhigljavsky, Anatoly
5502a431-c58e-4746-95f0-99191b535a6d
Whitaker, Roger
bdbb580b-bc48-4d5c-9802-3e66db310a14
Fesenko, Ivan
582790ad-8c25-4b1c-86b4-26c1736acd80
Kremnizer, Kobi
71cac4ee-43c1-45a4-8f3b-51a1320300fd
Noonan, Jack
daf2f66a-dacf-4ab1-9daf-c3af8e84b067
Harper, Paul
8cba8a2d-4088-4112-abc9-da6100e414b9
Gillard, Jonathan
2f08dfd7-484a-4ff2-a53f-7de9cd657c51
Woolley, Thomas
3efb9252-ba81-4801-ac8e-714099405f4b
Gartner, Daniel
fbe94ad1-bea5-441c-89aa-c5327e450f4b
Grimsley, Jasmine
c7c166e1-dd16-44d0-bf45-5c227d59d51c
Arruda, Edilson de
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
Fedorov, Val
d0db368a-5f20-46f8-9352-b9480554bc1c
Crick MBE, Tom
fb9a29ff-3055-401d-bca0-a16e4426b3a9
Zhigljavsky, Anatoly
5502a431-c58e-4746-95f0-99191b535a6d
Whitaker, Roger
bdbb580b-bc48-4d5c-9802-3e66db310a14
Fesenko, Ivan
582790ad-8c25-4b1c-86b4-26c1736acd80
Kremnizer, Kobi
71cac4ee-43c1-45a4-8f3b-51a1320300fd
Noonan, Jack
daf2f66a-dacf-4ab1-9daf-c3af8e84b067
Harper, Paul
8cba8a2d-4088-4112-abc9-da6100e414b9
Gillard, Jonathan
2f08dfd7-484a-4ff2-a53f-7de9cd657c51
Woolley, Thomas
3efb9252-ba81-4801-ac8e-714099405f4b
Gartner, Daniel
fbe94ad1-bea5-441c-89aa-c5327e450f4b
Grimsley, Jasmine
c7c166e1-dd16-44d0-bf45-5c227d59d51c
Arruda, Edilson de
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
Fedorov, Val
d0db368a-5f20-46f8-9352-b9480554bc1c
Crick MBE, Tom
fb9a29ff-3055-401d-bca0-a16e4426b3a9

Zhigljavsky, Anatoly, Whitaker, Roger, Fesenko, Ivan, Kremnizer, Kobi, Noonan, Jack, Harper, Paul, Gillard, Jonathan, Woolley, Thomas, Gartner, Daniel, Grimsley, Jasmine, Arruda, Edilson de, Fedorov, Val and Crick MBE, Tom (2020) Generic probabilistic modelling and non-homogeneity issues for the UK epidemic of COVID-19. arXiv, [arXiv:2004.01991 [stat.AP]].

Record type: Article

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.

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More information

Published date: 4 April 2020
Keywords: stat.AP, physics.soc-ph, q-bio.PE

Identifiers

Local EPrints ID: 444560
URI: http://eprints.soton.ac.uk/id/eprint/444560
PURE UUID: a666ba62-930d-4529-bede-3d9f7af68033
ORCID for Edilson de Arruda: ORCID iD orcid.org/0000-0002-9835-352X

Catalogue record

Date deposited: 26 Oct 2020 17:30
Last modified: 13 Dec 2021 03:39

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Contributors

Author: Anatoly Zhigljavsky
Author: Roger Whitaker
Author: Ivan Fesenko
Author: Kobi Kremnizer
Author: Jack Noonan
Author: Paul Harper
Author: Jonathan Gillard
Author: Thomas Woolley
Author: Daniel Gartner
Author: Jasmine Grimsley
Author: Val Fedorov
Author: Tom Crick MBE

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