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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, Ciara E; David Abrahams, I; Budd, Chris; Butchers, Matt; Cates, Michael E; Champneys, Alan R; Currie, Christine S M; Enright, Jessica; Gog, Julia R; Goriely, Alain; Déirdre Hollingsworth, T; Hoyle, Rebecca B; Isham, Valerie; Jordan, Joanna; Kaouri, Maha H; Kavoussanakis, Kostas; Leeks, Jane; Maini, Philip K; Marr, Christie; Merritt, Clare; Mollison, Denis; Ray, Surajit; Thompson, Robin N; Wakefield, Alexandra; Wasley, Dawn.
  • Dangerfield CE; Isaac Newton Institute to Mathematical Sciences, University of Cambridge, United Kingdom; Joint UNIversities Pandemic and Epidemiological Research (JUNIPER) Consortium, United Kingdom(1). Electronic address: ced57@cam.ac.uk.
  • David Abrahams I; Department for Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom.
  • Budd C; Department of Mathematics, University of Bath, United Kingdom.
  • Butchers M; Department of Mathematics, University of Bath, United Kingdom.
  • Cates ME; Department for Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom.
  • Champneys AR; Department of Engineering Mathematics, University of Bristol, United Kingdom.
  • Currie CSM; School of Mathematical Sciences, University of Southampton, United Kingdom.
  • Enright J; School of Computing Science, University of Glasgow, United Kingdom.
  • Gog JR; Joint UNIversities Pandemic and Epidemiological Research (JUNIPER) Consortium, United Kingdom(1); Department for Applied Mathematics and Theoretical Physics, University of Cambridge, United Kingdom.
  • Goriely A; Mathematical Institute, University of Oxford, United Kingdom.
  • Déirdre Hollingsworth T; Joint UNIversities Pandemic and Epidemiological Research (JUNIPER) Consortium, United Kingdom(1); Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, United Kingdom.
  • Hoyle RB; School of Mathematical Sciences, University of Southampton, United Kingdom.
  • Ini Professional Services; Isaac Newton Institute to Mathematical Sciences, University of Cambridge, United Kingdom.
  • Isham V; Department of Statistical Science, University College London, United Kingdom.
  • Jordan J; Independent, United Kingdom.
  • Kaouri MH; Isaac Newton Institute to Mathematical Sciences, University of Cambridge, United Kingdom.
  • Kavoussanakis K; EPCC, The University of Edinburgh, United Kingdom.
  • Leeks J; Isaac Newton Institute to Mathematical Sciences, University of Cambridge, United Kingdom.
  • Maini PK; Mathematical Institute, University of Oxford, United Kingdom.
  • Marr C; Isaac Newton Institute to Mathematical Sciences, University of Cambridge, United Kingdom.
  • Merritt C; Isaac Newton Institute to Mathematical Sciences, University of Cambridge, United Kingdom.
  • Mollison D; Department of Actuarial Mathematics and Statistics, Heriot-Watt University, United Kingdom.
  • Ray S; School of Mathematics and Statistics, University of Glasgow, United Kingdom.
  • Thompson RN; Mathematics Institute, University of Warwick, United Kingdom; Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, University of Warwick, United Kingdom.
  • Wakefield A; The Royal Society, London, United Kingdom.
  • Wasley D; International Centre for Mathematical Sciences, University of Edinburgh & Heriot-Watt University, United Kingdom.
J Theor Biol ; 557: 111332, 2023 01 21.
Article in English | MEDLINE | ID: covidwho-2313934
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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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Type of study: Observational study Limits: Humans Country/Region as subject: Europa Language: English Journal: J Theor Biol Year: 2023 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pandemics / COVID-19 Type of study: Observational study Limits: Humans Country/Region as subject: Europa Language: English Journal: J Theor Biol Year: 2023 Document Type: Article