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

Dangerfield, C. E. et al. (2023) 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) (PMID:36323393)

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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.

Item Type:Articles
Additional 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.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Enright, Dr Jessica and Ray, Professor Surajit
Authors: Dangerfield, C. E., Abrahams, I. D., Budd, C., Butchers, M., Cates, M. E., Champneys, A. R., Currie, C. S.M., Enright, J., Gog, J. R., Goriely, A., Hollingsworth, T. D., Hoyle, R. B., INI Professional Services, ., Isham, V., Jordan, J., Kaouri, M., Kavoussanakis, K., Leeks, J., Maini, P. K., Marr, C., Merritt, C., Mollison, D., Ray, S., Thompson, R. N., Wakefield, A., and Wasley, D.
College/School:College of Science and Engineering > School of Computing Science
College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Journal of Theoretical Biology
Publisher:Elsevier
ISSN:0022-5193
ISSN (Online):1095-8541
Published Online:30 October 2022
Copyright Holders:Copyright © 2022 The Author(s)
First Published:First published in Journal of Theoretical Biology 557: 111332
Publisher Policy:Reproduced under a Creative Commons License

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