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dc.contributor.authorThompson, RN
dc.contributor.authorHollingsworth, TD
dc.contributor.authorIsham, V
dc.contributor.authorArribas-Bel, D
dc.contributor.authorAshby, B
dc.contributor.authorBritton, T
dc.contributor.authorChalloner, P
dc.contributor.authorChappell, LHK
dc.contributor.authorClapham, H
dc.contributor.authorCunniffe, NJ
dc.contributor.authorDawid, AP
dc.contributor.authorDonnelly, CA
dc.contributor.authorEggo, R
dc.contributor.authorFunk, S
dc.contributor.authorGilbert, N
dc.contributor.authorGog, JR
dc.contributor.authorGlendinning, P
dc.contributor.authorHart, WS
dc.contributor.authorHeesterbeek, H
dc.contributor.authorHouse, T
dc.contributor.authorKeeling, M
dc.contributor.authorKiss, IZ
dc.contributor.authorKretzschmar, M
dc.contributor.authorLloyd, AL
dc.contributor.authorMcBryde, ES
dc.contributor.authorMcCaw, JM
dc.contributor.authorMiller, JC
dc.contributor.authorMcKinley, TJ
dc.contributor.authorMorris, M
dc.contributor.authorONeill, PD
dc.contributor.authorPearson, CAB
dc.contributor.authorParag, KV
dc.contributor.authorPellis, L
dc.contributor.authorPulliam, JRC
dc.contributor.authorRoss, JV
dc.contributor.authorTildesley, MJ
dc.contributor.authorTomba, GS
dc.contributor.authorSilverman, BW
dc.contributor.authorStruchiner, CJ
dc.contributor.authorTrapman, P
dc.contributor.authorWebb, CR
dc.contributor.authorMollison, D
dc.contributor.authorRestif, O
dc.date.accessioned2020-07-24T09:35:27Z
dc.date.issued2020-08-12
dc.description.abstractCombinations of intense non-pharmaceutical interventions ('lockdowns') were introduced in countries worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement lockdown exit strategies that allow restrictions to be relaxed 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, will 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. The roadmap requires a global collaborative effort from the scientific community and policy-makers, and is made up of three parts: i) improve estimation of key epidemiological parameters; ii) understand sources of heterogeneity in populations; 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.en_GB
dc.description.sponsorshipAlan Turing Instituteen_GB
dc.description.sponsorshipEPSRCen_GB
dc.identifier.citationVol. 287 (1932), article 20201405en_GB
dc.identifier.doi10.1098/rspb.2020.1405
dc.identifier.grantnumberEP/R014604/1en_GB
dc.identifier.urihttp://hdl.handle.net/10871/122129
dc.language.isoenen_GB
dc.publisherRoyal Societyen_GB
dc.rights© 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.
dc.subjectCOVID-19en_GB
dc.subjectSARS-CoV-2en_GB
dc.subjectexit strategyen_GB
dc.subjectmathematical modellingen_GB
dc.subjectepidemic controlen_GB
dc.subjectuncertaintyen_GB
dc.titleKey questions for modelling COVID-19 exit strategiesen_GB
dc.typeArticleen_GB
dc.date.available2020-07-24T09:35:27Z
dc.identifier.issn0962-8452
dc.descriptionThis is the final version. Available on open access from the Royal Society via the DOI in this recorden_GB
dc.identifier.eissn1471-2954
dc.identifier.journalProceedings of the Royal Society B: Biological Sciencesen_GB
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
dcterms.dateAccepted2020-06-21
exeter.funder::Alan Turing Instituteen_GB
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2020-06-21
rioxxterms.typeJournal Article/Reviewen_GB
refterms.dateFCD2020-07-24T09:28:12Z
refterms.versionFCDAM
refterms.dateFOA2020-08-27T12:22:53Z
refterms.panelBen_GB


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© 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.
Except where otherwise noted, this item's licence is described as © 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.