Getting the most out of maths: How to coordinate mathematical modelling research to support a pandemic, lessons learnt from COVID-19

Ciara Dangerfield (Lead Author), I. David Abrahams, Chris Budd, Matt Butchers, Michael Cates, Alan R. Champneys, Christine S. M. Currie, Jessica Enright, Julia R. Gog, Alain Goriely, T Déirdre Hollingsworth, Rebecca B. Hoyle, INI Professional Services, Valerie Isham, Jo Jordan, Maha Kaouri, Kostas Kavoussanakis, Jane Leeks, Philip K. Maini, Christie MarrClare Merritt, Denis Mollison, Surajit Ray, Robin N. Thompson, Alex Wakefield, Dawn Wasley

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

In March 2020 mathematics took centre stage in the fight against the COVID-19 pandemic. 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 relatively small epidemic 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 epidemic modelling needed to be coordinated to provide much-needed support, and to limit the burden on epidemic 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 and wider science research communities 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.
Original languageEnglish
JournalJournal of Theoretical Biology
Publication statusAccepted/In press - 18 Oct 2022

Keywords / Materials (for Non-textual outputs)

  • COVID-19
  • Mathematical modelling
  • Research co-ordination
  • Knowledge exchange


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