Journal article

Key questions for modelling COVID-19 exit strategies

Robin N Thompson, T Deirdre Hollingsworth, Valerie Isham, Daniel Arribas-Bel, Ben Ashby, Tom Britton, Peter Challenor, Lauren HK Chappell, Hannah Clapham, Nik J Cunniffe, A Philip Dawid, Christl A Donnelly, Rosalind M Eggo, Sebastian Funk, Nigel Gilbert, Paul Glendinning, Julia R Gog, William S Hart, Hans Heesterbeek, Thomas House Show all

Proceedings of the Royal Society B: Biological Sciences | ROYAL SOC | Published : 2020

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 tha..

View full abstract

Grants

Awarded by Isaac Newton Institute (EPSRC)


Awarded by Wellcome Trust


Awarded by BBSRC


Awarded by Natural Environment Research Council


Awarded by DFID


Awarded by HDR UK


Awarded by UK MRC


Awarded by Netherlands Organization for Health Research and Development (ZonMw)


Awarded by Royal Society


Awarded by Leverhulme Trust


Awarded by NTDModelling Consortium by the Bill and Melinda Gates Foundation


Awarded by Vetenskapsradet Swedish Research Council


Funding Acknowledgements

This work was supported by the Isaac Newton Institute (EPSRC grant no. EP/R014604/1). R.N.T. thanks Christ Church (Oxford) for funding via a Junior Research Fellowship. R.N.T. and S.F. acknowledge support from the Wellcome Trust (grant no. 210758/Z/18/Z). L.H.K.C. acknowledges support from the BBSRC (grant no. BB/R009236/1). B.A. is supported by the Natural Environment Research Council (grant no. NE/N014979/1). C.A.D. and K.V.P. thank the UK MRC and DFID for centre funding (grant no. MR/R015600/1). C.A.D. also thanks the UK NIHR (National Institute for Health Research) HPRU (Health Protection Research Unit). R.M.E. acknowledges HDR UK (grant no. MR/S003975/1) and the UK MRC (grant no. MC_PC 19065). H.H. and M.E.K. acknowledge support from the Netherlands Organization for Health Research and Development (ZonMw; grant no. 10430022010001). T.H. acknowledges support from the Royal Society (grant no. INF\R2\180067) and the Alan Turing Institute for Data Science and Artificial Intelligence. M.K. and M.J.T. acknowledge support from the UK MRC (grant no. MR/V009761/1). I.Z.K. acknowledges support from the Leverhulme Trust (grant no. RPG-2017-370). M.E.K. acknowledges support from the Netherlands Organization for Health Research and Development (ZonMw; grant no. 91216062). J.C.M. acknowledges start-up funding from La Trobe University. C.A.B.P. acknowledges funding of the NTDModelling Consortium by the Bill and Melinda Gates Foundation (grant no. OPP1184344). L.P. acknowledges support from the Wellcome Trust and the Royal Society (grant no. 202562/Z/16/Z). J.R.C.P. acknowledges support from the South African Centre for Epidemiological Modelling and Analysis (SACEMA), a Department of Science and Innovation-National Research Foundation Centre of Excellence hosted at Stellenbosch University. C.J.S. acknowledges support from CNPq and FAPERJ. P.T. acknowledges support from Vetenskapsradet Swedish Research Council (grant no. 2016-04566).