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Key questions for modelling COVID-19 exit strategies

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.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1098/rspb.2020.1405

Authors

More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MSD
Department:
NDM
Sub department:
Big Data Institute
Role:
Author
ORCID:
0000-0001-5962-4238


Publisher:
Royal Society
Journal:
Proceedings of the Royal Society B: Biological Sciences More from this journal
Volume:
287
Issue:
1932
Article number:
20201405
Publication date:
2020-08-12
Acceptance date:
2020-07-21
DOI:
EISSN:
1471-2954
ISSN:
0962-8452


Language:
English
Keywords:
Pubs id:
1116154
Local pid:
pubs:1116154
Deposit date:
2020-07-27
ARK identifier:

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