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).
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”.
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