The University of Southampton
University of Southampton Institutional Repository

Modelling and optimal control of multi strain epidemics, with application to COVID-19

Modelling and optimal control of multi strain epidemics, with application to COVID-19
Modelling and optimal control of multi strain epidemics, with application to COVID-19
This work introduces a novel epidemiological model that simultaneously considers multiple viral strains, reinfections due to waning immunity response over time and an optimal control formulation. This enables us to derive optimal mitigation strategies over a prescribed time horizon under a more realistic framework that does not imply perennial immunity and a single strain, although these can also be derived as particular cases of our formulation. The model also allows estimation of the number of infections over time in the absence of mitigation strategies under any number of viral strains. We validate our approach in the light of the COVID-19 epidemic and present a number of experiments to shed light on the overall behaviour under one or two strains in the absence of sufficient mitigation measures. We also derive optimal control strategies for distinct mitigation costs and evaluate the effect of these costs on the optimal mitigation measures over a two-year horizon. The results show that relaxations in the mitigation measures cause a rapid increase in the number of cases, which then demand more restrictive measures in the future.
eess.SY, cs.SY, q-bio.QM, 49, 92-10
2331-8422
Arruda, Edilson F.
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
Pastore, Dayse H.
50335f89-5fe1-4f90-9b7d-59647d652942
Dias, Clauda M.
2b0f2626-74aa-4fa0-93d1-4575c778a8a4
Das, Shyam S.
b5e8d90c-67b9-4d49-979e-d75e385eaf24
Arruda, Edilson F.
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
Pastore, Dayse H.
50335f89-5fe1-4f90-9b7d-59647d652942
Dias, Clauda M.
2b0f2626-74aa-4fa0-93d1-4575c778a8a4
Das, Shyam S.
b5e8d90c-67b9-4d49-979e-d75e385eaf24

Arruda, Edilson F., Pastore, Dayse H., Dias, Clauda M. and Das, Shyam S. (2021) Modelling and optimal control of multi strain epidemics, with application to COVID-19. arXiv.

Record type: Article

Abstract

This work introduces a novel epidemiological model that simultaneously considers multiple viral strains, reinfections due to waning immunity response over time and an optimal control formulation. This enables us to derive optimal mitigation strategies over a prescribed time horizon under a more realistic framework that does not imply perennial immunity and a single strain, although these can also be derived as particular cases of our formulation. The model also allows estimation of the number of infections over time in the absence of mitigation strategies under any number of viral strains. We validate our approach in the light of the COVID-19 epidemic and present a number of experiments to shed light on the overall behaviour under one or two strains in the absence of sufficient mitigation measures. We also derive optimal control strategies for distinct mitigation costs and evaluate the effect of these costs on the optimal mitigation measures over a two-year horizon. The results show that relaxations in the mitigation measures cause a rapid increase in the number of cases, which then demand more restrictive measures in the future.

Text
2101.08137v1 - Author's Original
Download (1MB)

More information

Accepted/In Press date: 19 January 2021
Published date: 19 January 2021
Keywords: eess.SY, cs.SY, q-bio.QM, 49, 92-10

Identifiers

Local EPrints ID: 447536
URI: http://eprints.soton.ac.uk/id/eprint/447536
ISSN: 2331-8422
PURE UUID: d671babd-96d1-48de-a811-a4fb64ef79a2
ORCID for Edilson F. Arruda: ORCID iD orcid.org/0000-0002-9835-352X

Catalogue record

Date deposited: 15 Mar 2021 17:39
Last modified: 13 Dec 2021 03:40

Export record

Contributors

Author: Dayse H. Pastore
Author: Clauda M. Dias
Author: Shyam S. Das

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×