Key questions for modelling COVID-19 exit strategies

Author:

Thompson Robin N.123ORCID,Hollingsworth T. Déirdre4ORCID,Isham Valerie5,Arribas-Bel Daniel67,Ashby Ben8ORCID,Britton Tom9,Challenor Peter10ORCID,Chappell Lauren H. K.11,Clapham Hannah12,Cunniffe Nik J.13ORCID,Dawid A. Philip14ORCID,Donnelly Christl A.1516,Eggo Rosalind M.3ORCID,Funk Sebastian3ORCID,Gilbert Nigel17ORCID,Glendinning Paul18,Gog Julia R.19,Hart William S.1ORCID,Heesterbeek Hans20,House Thomas2122ORCID,Keeling Matt23ORCID,Kiss István Z.24ORCID,Kretzschmar Mirjam E.25,Lloyd Alun L.26ORCID,McBryde Emma S.27,McCaw James M.28ORCID,McKinley Trevelyan J.29,Miller Joel C.30,Morris Martina31,O'Neill Philip D.32,Parag Kris V.16,Pearson Carl A. B.333ORCID,Pellis Lorenzo19,Pulliam Juliet R. C.33,Ross Joshua V.34ORCID,Tomba Gianpaolo Scalia35,Silverman Bernard W.1536ORCID,Struchiner Claudio J.37,Tildesley Michael J.23,Trapman Pieter9ORCID,Webb Cerian R.13,Mollison Denis38,Restif Olivier39ORCID

Affiliation:

1. Mathematical Institute, University of Oxford, Woodstock Road, Oxford OX2 6GG, UK

2. Christ Church, University of Oxford, St Aldates, Oxford OX1 1DP, UK

3. Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, Keppel Street, London WC1E 7HT, UK

4. Big Data Institute, University of Oxford, Old Road Campus, Oxford OX3 7LF, UK

5. Department of Statistical Science, University College London, Gower Street, London WC1E 6BT, UK

6. School of Environmental Sciences, University of Liverpool, Brownlow Street, Liverpool L3 5DA, UK

7. The Alan Turing Institute, British Library, 96 Euston Road, London NW1 2DB, UK

8. Department of Mathematical Sciences, University of Bath, North Road, Bath BA2 7AY, UK

9. Department of Mathematics, Stockholm University, Kräftriket, 106 91 Stockholm, Sweden

10. College of Engineering, Mathematical and Physical Sciences, University of Exeter, Exeter EX4 4QE, UK

11. Department of Plant Sciences, University of Oxford, South Parks Road, Oxford OX1 3RB, UK

12. Saw Swee Hock School of Public Health, National University of Singapore, 12 Science Drive, Singapore 117549, Singapore

13. Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK

14. Statistical Laboratory, University of Cambridge, Wilberforce Road, Cambridge CB3 0WB, UK

15. Department of Statistics, University of Oxford, St Giles', Oxford OX1 3LB, UK

16. MRC Centre for Global Infectious Disease Analysis, Department of Infectious Disease Epidemiology, Imperial College London, Norfolk Place, London W2 1PG, UK

17. Department of Sociology, University of Surrey, Stag Hill, Guildford GU2 7XH, UK

18. Department of Mathematics, University of Manchester, Oxford Road, Manchester M13 9PL, UK

19. Centre for Mathematical Sciences, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, UK

20. Department of Population Health Sciences, Utrecht University, Yalelaan, 3584 CL Utrecht, The Netherlands

21. IBM Research, The Hartree Centre, Daresbury, Warrington WA4 4AD, UK

22. Mathematics Institute, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK

23. Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research, School of Life Sciences and Mathematics Institute, University of Warwick, Gibbet Hill Road, Coventry CV4 7AL, UK

24. School of Mathematical and Physical Sciences, University of Sussex, Falmer, Brighton BN1 9QH, UK

25. Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, 3584CX Utrecht, The Netherlands

26. Biomathematics Graduate Program and Department of Mathematics, North Carolina State University, Raleigh, NC 27695, USA

27. Australian Institute of Tropical Health and Medicine, James Cook University, Townsville, Queensland 4811, Australia

28. School of Mathematics and Statistics, University of Melbourne, Carlton, Victoria 3010, Australia

29. College of Medicine and Health, University of Exeter, Barrack Road, Exeter EX2 5DW, UK

30. Department of Mathematics and Statistics, La Trobe University, Bundoora, Victoria 3086, Australia

31. Department of Sociology, University of Washington, Savery Hall, Seattle, WA 98195, USA

32. School of Mathematical Sciences, University of Nottingham, University Park, Nottingham NG7 2RD, UK

33. South African DSI-NRF Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), Stellenbosch University, Jonkershoek Road, Stellenbosch 7600, South Africa

34. School of Mathematical Sciences, University of Adelaide, South Australia 5005, Australia

35. Department of Mathematics, University of Rome Tor Vergata, 00133 Rome, Italy

36. Rights Lab, University of Nottingham, Highfield House, Nottingham NG7 2RD, UK

37. Escola de Matemática Aplicada, Fundação Getúlio Vargas, Praia de Botafogo, 190 Rio de Janeiro, Brazil

38. Department of Actuarial Mathematics and Statistics, Heriot-Watt University, Edinburgh EH14 4AS, UK

39. Department of Veterinary Medicine, University of Cambridge, Madingley Road, Cambridge CB3 0ES, UK

Abstract

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute ‘Models for an exit strategy’ workshop (11–15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

Funder

BBSRC

Wellcome Trust

Bill and Melinda Gates Foundation

HDR

Leverhulme Trust

Engineering and Physical Sciences Research Council

Royal Society

Christ Church

NERC

Vetenskapsradet

ZonMw

MRC

Publisher

The Royal Society

Subject

General Agricultural and Biological Sciences,General Environmental Science,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine

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