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Modelling pre-symptomatic infectiousness in Covid-19

Modelling pre-symptomatic infectiousness in Covid-19
Modelling pre-symptomatic infectiousness in Covid-19

This paper considers SEPIR, an extension of the well-known SEIR continuous simulation compartment model. Both models can be fitted to real data as they include parameters that can be estimated from the data. SEPIR deploys an additional pre-symptomatic (also called asymptomatic) infectious stage not in SEIR but known to exist in COVID-19. This stage can also be fitted to data. We focus on how to fit SEPIR to a first wave. Both SEPIR and the existing SEIR model assume a homogeneous mixing population with parameters fixed. Moreover neither includes dynamically varying control strategies deployed against the virus. If either model is to represent more than just a single wave of the epidemic, then the parameters of the model would have to be time dependent. In view of this we also consider how reproduction numbers can be calculated to investigate the longer term overall result of an epidemic.

Asymptomatic transmission, Differential equation epidemic models, Effective reproduction number, Parametric models
221-230
Operational Research Society
Cheng, Russell
a4296b4e-7693-4e5f-b3d5-27b617bb9d67
Dagpunar, John
be796c6f-4b91-462b-b7ef-c9387efc26dc
Dye, Christopher
73ab1d1e-80ee-496e-9765-6842739fd843
Williams, Brian
b92e9a04-6b8a-4a8e-9661-9d89898f9273
Fakhimi, Masoud
Boness, Tom
Robertson, Duncan
Cheng, Russell
a4296b4e-7693-4e5f-b3d5-27b617bb9d67
Dagpunar, John
be796c6f-4b91-462b-b7ef-c9387efc26dc
Dye, Christopher
73ab1d1e-80ee-496e-9765-6842739fd843
Williams, Brian
b92e9a04-6b8a-4a8e-9661-9d89898f9273
Fakhimi, Masoud
Boness, Tom
Robertson, Duncan

Cheng, Russell, Dagpunar, John, Dye, Christopher and Williams, Brian (2021) Modelling pre-symptomatic infectiousness in Covid-19. Fakhimi, Masoud, Boness, Tom and Robertson, Duncan (eds.) In Operational Research Society 10th Simulation Workshop, SW 2021 - Proceedings. Operational Research Society. pp. 221-230 . (doi:10.36819/SW21.024).

Record type: Conference or Workshop Item (Paper)

Abstract

This paper considers SEPIR, an extension of the well-known SEIR continuous simulation compartment model. Both models can be fitted to real data as they include parameters that can be estimated from the data. SEPIR deploys an additional pre-symptomatic (also called asymptomatic) infectious stage not in SEIR but known to exist in COVID-19. This stage can also be fitted to data. We focus on how to fit SEPIR to a first wave. Both SEPIR and the existing SEIR model assume a homogeneous mixing population with parameters fixed. Moreover neither includes dynamically varying control strategies deployed against the virus. If either model is to represent more than just a single wave of the epidemic, then the parameters of the model would have to be time dependent. In view of this we also consider how reproduction numbers can be calculated to investigate the longer term overall result of an epidemic.

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More information

Published date: 22 March 2021
Additional Information: Publisher Copyright: © 2021 SW 2021. All rights reserved. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
Venue - Dates: 10th Operational Research Society Simulation Workshop, SW 2021, , Virtual, Online, 2021-03-22 - 2021-03-26
Keywords: Asymptomatic transmission, Differential equation epidemic models, Effective reproduction number, Parametric models

Identifiers

Local EPrints ID: 449946
URI: http://eprints.soton.ac.uk/id/eprint/449946
PURE UUID: 1f473901-352f-40e3-82ef-f35ea52f61be

Catalogue record

Date deposited: 28 Jun 2021 16:32
Last modified: 27 Apr 2022 09:41

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Contributors

Author: Russell Cheng
Author: John Dagpunar
Author: Christopher Dye
Author: Brian Williams
Editor: Masoud Fakhimi
Editor: Tom Boness
Editor: Duncan Robertson

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