The University of Southampton
University of Southampton Institutional Repository

Getting the most out of maths: how to coordinate mathematical modelling research to support a pandemic, lessons learnt from three initiatives that were part of the COVID-19 response in the UK

Getting the most out of maths: how to coordinate mathematical modelling research to support a pandemic, lessons learnt from three initiatives that were part of the COVID-19 response in the UK
Getting the most out of maths: how to coordinate mathematical modelling research to support a pandemic, lessons learnt from three initiatives that were part of the COVID-19 response in the UK

In March 2020 mathematics became a key part of the scientific advice to the UK government on the pandemic response to COVID-19. Mathematical and statistical modelling provided critical information on the spread of the virus and the potential impact of different interventions. The unprecedented scale of the challenge led the epidemiological modelling community in the UK to be pushed to its limits. At the same time, mathematical modellers across the country were keen to use their knowledge and skills to support the COVID-19 modelling effort. However, this sudden great interest in epidemiological modelling needed to be coordinated to provide much-needed support, and to limit the burden on epidemiological modellers already very stretched for time. In this paper we describe three initiatives set up in the UK in spring 2020 to coordinate the mathematical sciences research community in supporting mathematical modelling of COVID-19. Each initiative had different primary aims and worked to maximise synergies between the various projects. We reflect on the lessons learnt, highlighting the key roles of pre-existing research collaborations and focal centres of coordination in contributing to the success of these initiatives. We conclude with recommendations about important ways in which the scientific research community could be better prepared for future pandemics. This manuscript was submitted as part of a theme issue on “Modelling COVID-19 and Preparedness for Future Pandemics”.

COVID-19, Knowledge exchange, Mathematical modelling, Research co-ordination
0022-5193
Dangerfield, Ciara E.
ad6f8601-8ea5-4c94-8fd2-71631616a1fc
Abrahams, I. David
0d069308-d582-4f9b-be6d-b2d09840ee61
Budd, Chris
5197d25a-8a8f-4863-8b6c-c09eb21856c2
Butchers, Matt
1eed375a-d15d-4327-be9b-014906e60992
Cates, Michael E
ca9c48f8-22c8-44d3-bb13-9f70ca467024
Champneys, Alan R.
47a636dc-28d2-4d57-ad12-578d34661284
Currie, Christine S.M.
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Enright, Jessica
594ebd8c-0423-4c7c-a53d-dd563cac48ca
Gog, Julia R
31014bc8-ea85-4989-bcfe-9a7d4b1fe52b
Goriely, Alain
688eb80c-0c95-4af4-a659-e7ab02bd97dd
Hollingsworth, T. Déirdre
a186d0cc-8013-401c-879b-3a9370987e55
Hoyle, Rebecca
e980d6a8-b750-491b-be13-84d695f8b8a1
Services, INI Professional
0be9a309-2a67-4703-adc2-46932de8d84a
Isham, Valerie
7018d266-683e-4356-9c2c-a4cdada0fd2c
Jordan, Joanna
f1a120fd-0368-4394-9e2c-6b4e88383230
Kaouri, Maha
6c6947f1-43f0-4b1b-85a9-47247553b1d7
Kavoussanakis, Kostas
0806fa27-1d97-4d04-b83d-2f20754cde75
Leeks, Jane
5698b06f-6554-4624-ba5a-2e7d9a0bb255
Maini, Philip K
9938f55c-ac59-4f1c-87a6-4466fa9d8145
Marr, Christie
ad780f61-6749-4190-bd50-0dbf4b6c3485
Merritt, Clare
83b32165-6bcd-4739-ba37-f4a732b49cf4
Mollison, Denis
8737846e-36a9-4ac5-9453-ec384129ee41
Ray, Surajit
fc2fa16e-9854-44e9-9616-3e7c9d135761
Thompson, Robin N.
64e7720b-f34f-4dfb-8ebc-df9bd9411346
Wakefield, Alexandra
af49b3bf-6025-4d71-9d2f-edde2e17dace
Wasley, Dawn
9488116a-2c11-4a0a-b18d-022b276260cd
Dangerfield, Ciara E.
ad6f8601-8ea5-4c94-8fd2-71631616a1fc
Abrahams, I. David
0d069308-d582-4f9b-be6d-b2d09840ee61
Budd, Chris
5197d25a-8a8f-4863-8b6c-c09eb21856c2
Butchers, Matt
1eed375a-d15d-4327-be9b-014906e60992
Cates, Michael E
ca9c48f8-22c8-44d3-bb13-9f70ca467024
Champneys, Alan R.
47a636dc-28d2-4d57-ad12-578d34661284
Currie, Christine S.M.
dcfd0972-1b42-4fac-8a67-0258cfdeb55a
Enright, Jessica
594ebd8c-0423-4c7c-a53d-dd563cac48ca
Gog, Julia R
31014bc8-ea85-4989-bcfe-9a7d4b1fe52b
Goriely, Alain
688eb80c-0c95-4af4-a659-e7ab02bd97dd
Hollingsworth, T. Déirdre
a186d0cc-8013-401c-879b-3a9370987e55
Hoyle, Rebecca
e980d6a8-b750-491b-be13-84d695f8b8a1
Services, INI Professional
0be9a309-2a67-4703-adc2-46932de8d84a
Isham, Valerie
7018d266-683e-4356-9c2c-a4cdada0fd2c
Jordan, Joanna
f1a120fd-0368-4394-9e2c-6b4e88383230
Kaouri, Maha
6c6947f1-43f0-4b1b-85a9-47247553b1d7
Kavoussanakis, Kostas
0806fa27-1d97-4d04-b83d-2f20754cde75
Leeks, Jane
5698b06f-6554-4624-ba5a-2e7d9a0bb255
Maini, Philip K
9938f55c-ac59-4f1c-87a6-4466fa9d8145
Marr, Christie
ad780f61-6749-4190-bd50-0dbf4b6c3485
Merritt, Clare
83b32165-6bcd-4739-ba37-f4a732b49cf4
Mollison, Denis
8737846e-36a9-4ac5-9453-ec384129ee41
Ray, Surajit
fc2fa16e-9854-44e9-9616-3e7c9d135761
Thompson, Robin N.
64e7720b-f34f-4dfb-8ebc-df9bd9411346
Wakefield, Alexandra
af49b3bf-6025-4d71-9d2f-edde2e17dace
Wasley, Dawn
9488116a-2c11-4a0a-b18d-022b276260cd

Dangerfield, Ciara E., Abrahams, I. David, Budd, Chris, Butchers, Matt, Cates, Michael E, Champneys, Alan R., Currie, Christine S.M., Enright, Jessica, Gog, Julia R, Goriely, Alain, Hollingsworth, T. Déirdre, Hoyle, Rebecca, Services, INI Professional, Isham, Valerie, Jordan, Joanna, Kaouri, Maha, Kavoussanakis, Kostas, Leeks, Jane, Maini, Philip K, Marr, Christie, Merritt, Clare, Mollison, Denis, Ray, Surajit, Thompson, Robin N., Wakefield, Alexandra and Wasley, Dawn (2022) Getting the most out of maths: how to coordinate mathematical modelling research to support a pandemic, lessons learnt from three initiatives that were part of the COVID-19 response in the UK. Journal of Theoretical Biology, 557, [111332]. (doi:10.1016/j.jtbi.2022.111332).

Record type: Article

Abstract

In March 2020 mathematics became a key part of the scientific advice to the UK government on the pandemic response to COVID-19. Mathematical and statistical modelling provided critical information on the spread of the virus and the potential impact of different interventions. The unprecedented scale of the challenge led the epidemiological modelling community in the UK to be pushed to its limits. At the same time, mathematical modellers across the country were keen to use their knowledge and skills to support the COVID-19 modelling effort. However, this sudden great interest in epidemiological modelling needed to be coordinated to provide much-needed support, and to limit the burden on epidemiological modellers already very stretched for time. In this paper we describe three initiatives set up in the UK in spring 2020 to coordinate the mathematical sciences research community in supporting mathematical modelling of COVID-19. Each initiative had different primary aims and worked to maximise synergies between the various projects. We reflect on the lessons learnt, highlighting the key roles of pre-existing research collaborations and focal centres of coordination in contributing to the success of these initiatives. We conclude with recommendations about important ways in which the scientific research community could be better prepared for future pandemics. This manuscript was submitted as part of a theme issue on “Modelling COVID-19 and Preparedness for Future Pandemics”.

Text
1-s2.0-S002251932200323X-main - Version of Record
Available under License Creative Commons Attribution.
Download (4MB)

More information

Accepted/In Press date: 17 October 2022
e-pub ahead of print date: 30 October 2022
Published date: 14 November 2022
Additional Information: Funding Information: This work was supported by INI EPSRC grant no EP/R014604/1, the RAMP continuity network grant EP/V053507/1, UKRI grant ST/V00221X/1, UKRI JUNIPER modelling consortium grant number MR/V038613/1, ICMS EPRSC grant no EP/R015007/1.
Keywords: COVID-19, Knowledge exchange, Mathematical modelling, Research co-ordination

Identifiers

Local EPrints ID: 472268
URI: http://eprints.soton.ac.uk/id/eprint/472268
ISSN: 0022-5193
PURE UUID: 9da9cde3-9b42-47e3-818d-075d2ba3f4b4
ORCID for Christine S.M. Currie: ORCID iD orcid.org/0000-0002-7016-3652
ORCID for Rebecca Hoyle: ORCID iD orcid.org/0000-0002-1645-1071

Catalogue record

Date deposited: 30 Nov 2022 17:42
Last modified: 01 Dec 2022 02:42

Export record

Altmetrics

Contributors

Author: Ciara E. Dangerfield
Author: I. David Abrahams
Author: Chris Budd
Author: Matt Butchers
Author: Michael E Cates
Author: Alan R. Champneys
Author: Jessica Enright
Author: Julia R Gog
Author: Alain Goriely
Author: T. Déirdre Hollingsworth
Author: Rebecca Hoyle ORCID iD
Author: INI Professional Services
Author: Valerie Isham
Author: Joanna Jordan
Author: Maha Kaouri
Author: Kostas Kavoussanakis
Author: Jane Leeks
Author: Philip K Maini
Author: Christie Marr
Author: Clare Merritt
Author: Denis Mollison
Author: Surajit Ray
Author: Robin N. Thompson
Author: Alexandra Wakefield
Author: Dawn Wasley

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.

×