The University of Southampton
University of Southampton Institutional Repository

How simulation modelling can help reduce the impact of COVID-19

How simulation modelling can help reduce the impact of COVID-19
How simulation modelling can help reduce the impact of COVID-19
Modelling has been used extensively by all national governments and the World Health Organization in deciding on the best strategies to pursue in mitigating the effects of COVID-19. Principally these have been epidemiological models aimed at understanding the spread of the disease and the impacts of different interventions. But a global pandemic generates a large number of problems and questions, not just those related to disease transmission, and each requires a different model to find the best solution. In this article we identify challenges resulting from the COVID-19 pandemic and discuss how simulation modelling could help to support decision-makers in making the most informed decisions. Modellers should see the article as a call to arms and decision-makers as a guide to what support is available from the simulation community.
COVID-19, coronavirus, pandemic, simulation modelling
1747-7778
83-97
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Fowler, John
7b961910-d249-4184-98b9-5f4bd10ecdde
Kotiadis, Kathy
560213cd-23c8-4fd1-ad47-06de52cccffc
Monks, Thomas
c675ef05-95c4-451d-a17a-11c5a2192427
Onggo, Bhakti Stephan
8e9a2ea5-140a-44c0-9c17-e9cf93662f80
Robertson, Duncan
e71015d7-24e5-429d-ac4c-25ce5f88ae29
Tako, Antuela
ad11c5e0-e9b5-45cf-a3ad-9ff3fab9c4eb
Currie, Christine
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Fowler, John
7b961910-d249-4184-98b9-5f4bd10ecdde
Kotiadis, Kathy
560213cd-23c8-4fd1-ad47-06de52cccffc
Monks, Thomas
c675ef05-95c4-451d-a17a-11c5a2192427
Onggo, Bhakti Stephan
8e9a2ea5-140a-44c0-9c17-e9cf93662f80
Robertson, Duncan
e71015d7-24e5-429d-ac4c-25ce5f88ae29
Tako, Antuela
ad11c5e0-e9b5-45cf-a3ad-9ff3fab9c4eb

Currie, Christine, Fowler, John, Kotiadis, Kathy, Monks, Thomas, Onggo, Bhakti Stephan, Robertson, Duncan and Tako, Antuela (2020) How simulation modelling can help reduce the impact of COVID-19. Journal of Simulation, 14 (2), 83-97. (doi:10.1080/17477778.2020.1751570).

Record type: Article

Abstract

Modelling has been used extensively by all national governments and the World Health Organization in deciding on the best strategies to pursue in mitigating the effects of COVID-19. Principally these have been epidemiological models aimed at understanding the spread of the disease and the impacts of different interventions. But a global pandemic generates a large number of problems and questions, not just those related to disease transmission, and each requires a different model to find the best solution. In this article we identify challenges resulting from the COVID-19 pandemic and discuss how simulation modelling could help to support decision-makers in making the most informed decisions. Modellers should see the article as a call to arms and decision-makers as a guide to what support is available from the simulation community.

Text
How Simulation Modelling Can Help Reduce the Impact of COVID19 Resubmitted Correct Refs - Accepted Manuscript
Download (434kB)

More information

Accepted/In Press date: 31 March 2020
e-pub ahead of print date: 15 April 2020
Keywords: COVID-19, coronavirus, pandemic, simulation modelling

Identifiers

Local EPrints ID: 439184
URI: http://eprints.soton.ac.uk/id/eprint/439184
ISSN: 1747-7778
PURE UUID: a93b5b66-47f4-462a-9acf-306256cb419f
ORCID for Christine Currie: ORCID iD orcid.org/0000-0002-7016-3652
ORCID for Bhakti Stephan Onggo: ORCID iD orcid.org/0000-0001-5899-304X

Catalogue record

Date deposited: 06 Apr 2020 16:35
Last modified: 28 Apr 2022 04:34

Export record

Altmetrics

Contributors

Author: John Fowler
Author: Kathy Kotiadis
Author: Thomas Monks
Author: Duncan Robertson
Author: Antuela Tako

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.

×