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 of London Series B, Biological Sciences, 287 (1932).

[img]
Preview
PDF (Published Version) - Published Version
Available under License Creative Commons Attribution.

Download (528kB) | Preview
View at Publisher Website: https://doi.org/10.1098/rspb.2020.1405
 
18
15


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.

Item ID: 67170
Item Type: Article (Research - C1)
ISSN: 1471-2954
Keywords: COVID-19; SARS-CoV-2; Exit strategy; Mathematical modelling; Epidemic control; Uncertainty
Copyright Information: © 2020 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
Funders: Isaac Newton Institute (INI), Christ Church Oxford (CCO), Wellcome Trust (WT), Biotechnology and Biological Sciences Research Council (BBSRC), Natural Environment Research Council (NERC), UK Medical Research Council (MRC), UK Department for International Development (DFID), UK National Institute for Health Research (NIHR), UK Health Protection Research Unit (HPRU), Health Data Research UK (HDRUK), Netherlands Organization for Health Research and Development (NOHRD), Royal Society (RS), Alan Turing Institute for Data Science and Artificial Intelligence, Leverhulme Trust (LT), La Trobe University (LTU), Bill and Melinda Gates Foundation (BMGF), South African Centre for Epidemiological Modelling and Analysis (SACEMA), National Council for Scientific and Technological Development (CNPq), Fundação de Amparo a Pesquisa do Estado do Rio de Janeiro (FAPERJ), Vetenskapsrådet Swedish Research Council (VSRC)
Projects and Grants: INI EPSRC grant no. EP/R014604/1, CCO Junior Research Fellowship, WT grant no. 210758/Z/18/Z, BBSRC grant no. BB/R009236/1, NERC grant no. NE/N014979/1, MRC & DFID grant no. MR/R01500/1, HDRUK grant no. MR/S003975/1, MRC grant no. MC_PC 19065, NOHRD grant no. 1043002201000, RS grant no. INF\R2\180067, MRC grant no. MR/V009761/1, LT grant no. RPG-2017-37, NOHRD grant no. 91216062, BMGF grant no. OPP1184344, WT & RS grant no. 202562/Z/16/Z, VSRC grant no. 2016-04566
Date Deposited: 15 Mar 2021 23:13
FoR Codes: 49 MATHEMATICAL SCIENCES > 4901 Applied mathematics > 490102 Biological mathematics @ 30%
42 HEALTH SCIENCES > 4202 Epidemiology > 420299 Epidemiology not elsewhere classified @ 70%
SEO Codes: 20 HEALTH > 2004 Public health (excl. specific population health) > 200404 Disease distribution and transmission (incl. surveillance and response) @ 80%
20 HEALTH > 2002 Evaluation of health and support services > 200205 Health policy evaluation @ 20%
Downloads: Total: 15
Last 12 Months: 15
More Statistics

Actions (Repository Staff Only)

Item Control Page Item Control Page