| Literature DB >> 32781946 |
Robin N Thompson1,2,3, T Déirdre Hollingsworth4, Valerie Isham5, Daniel Arribas-Bel6,7, Ben Ashby8, Tom Britton9, Peter Challenor10, Lauren H K Chappell11, Hannah Clapham12, Nik J Cunniffe13, A Philip Dawid14, Christl A Donnelly15,16, Rosalind M Eggo3, Sebastian Funk3, Nigel Gilbert17, Paul Glendinning18, Julia R Gog19, William S Hart1, Hans Heesterbeek20, Thomas House21,22, Matt Keeling23, István Z Kiss24, Mirjam E Kretzschmar25, Alun L Lloyd26, Emma S McBryde27, James M McCaw28, Trevelyan J McKinley29, Joel C Miller30, Martina Morris31, Philip D O'Neill32, Kris V Parag16, Carl A B Pearson3,33, Lorenzo Pellis19, Juliet R C Pulliam33, Joshua V Ross34, Gianpaolo Scalia Tomba35, Bernard W Silverman15,36, Claudio J Struchiner37, Michael J Tildesley23, Pieter Trapman9, Cerian R Webb13, Denis Mollison38, Olivier Restif39.
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.Entities:
Keywords: COVID-19; SARS-CoV-2; epidemic control; exit strategy; mathematical modelling; uncertainty
Mesh:
Year: 2020 PMID: 32781946 PMCID: PMC7575516 DOI: 10.1098/rspb.2020.1405
Source DB: PubMed Journal: Proc Biol Sci ISSN: 0962-8452 Impact factor: 5.349