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Key questions for modelling COVID-19 exit strategies

Thompson, Robin N., Hollingsworth, T. Déirdre, Isham, Valerie, Arribas-Bel, Daniel, Ashby, Ben, Britton, Tom, Challenor, Peter, Chappell, Lauren H. K., Clapham, Hannah, Cunniffe, Nik J. , Dawid, A. Philip, Donnelly, Christl A., Eggo, Rosalind M., Funk, Sebastian, Gilbert, Nigel, Glendinning, Paul, Gog, Julia R., Hart, William S., Heesterbeek, Hans, House, Thomas, Keeling, Matt, Kiss, István Z., Kretzschmar, Mirjam E., Lloyd, Alun L., McBryde, Emma S., McCaw, James M., McKinley, Trevelyan J., Miller, Joel C., Morris, Martina, O'Neill, Philip D., Parag, Kris V., Pearson, Carl A. B., Pellis, Lorenzo, Pulliam, Juliet R. C., Ross, Joshua V., Tomba, Gianpaolo Scalia, Silverman, Bernard W., Struchiner, Claudio J., Tildesley, Michael J., Trapman, Pieter, Webb, Cerian R., Mollison, Denis and Restif, Olivier (2020) Key questions for modelling COVID-19 exit strategies Proceedings of the Royal Society B: Biological Sciences, 287 (1932), 20201405.

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Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARSCoV- 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-tomiddle-income countries. This will provide important information for planning exit strategies that balance socioeconomic benefits with public health.

Item Type: Article
Divisions : Faculty of Arts and Social Sciences > Department of Sociology
Authors :
Thompson, Robin N.
Hollingsworth, T. Déirdre
Isham, Valerie
Arribas-Bel, Daniel
Ashby, Ben
Britton, Tom
Challenor, Peter
Chappell, Lauren H. K.
Clapham, Hannah
Cunniffe, Nik J.
Dawid, A. Philip
Donnelly, Christl A.
Eggo, Rosalind M.
Funk, Sebastian
Glendinning, Paul
Gog, Julia R.
Hart, William S.
Heesterbeek, Hans
House, Thomas
Keeling, Matt
Kiss, István Z.
Kretzschmar, Mirjam E.
Lloyd, Alun L.
McBryde, Emma S.
McCaw, James M.
McKinley, Trevelyan J.
Miller, Joel C.
Morris, Martina
O'Neill, Philip D.
Parag, Kris V.
Pearson, Carl A. B.
Pellis, Lorenzo
Pulliam, Juliet R. C.
Ross, Joshua V.
Tomba, Gianpaolo Scalia
Silverman, Bernard W.
Struchiner, Claudio J.
Tildesley, Michael J.
Trapman, Pieter
Webb, Cerian R.
Mollison, Denis
Restif, Olivier
Date : 12 August 2020
Funders : Isaac Newton Institute - EPSRC, Wellcome Trust, BBSRC, Natural Environment Research Council (NERC), MRC, HDR UK, UK MRC, Netherlands Organization for Health Research and Development, Royal Society, Bill and Melinda Gates Foundation, Vetenskapsrådet Swedish Research Council
DOI : 10.1098/rspb.2020.1405
Grant Title : EPSRC
Copyright Disclaimer : © 2020 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License, which permits unrestricted use, provided the original author and source are credited.
Uncontrolled Keywords : COVID-19, SARS-CoV-2, exit strategy, mathematical modelling, epidemic control, uncertainty
Additional Information : Embargo OK Metadata OK No Further Action
Depositing User : James Marshall
Date Deposited : 14 Aug 2020 09:26
Last Modified : 14 Aug 2020 09:26

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