Key questions for modelling COVID-19 exit strategies.
COVID-19
SARS-CoV-2
epidemic control
exit strategy
mathematical modelling
uncertainty
Journal
Proceedings. Biological sciences
ISSN: 1471-2954
Titre abrégé: Proc Biol Sci
Pays: England
ID NLM: 101245157
Informations de publication
Date de publication:
12 08 2020
12 08 2020
Historique:
entrez:
13
8
2020
pubmed:
13
8
2020
medline:
28
8
2020
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 32781946
doi: 10.1098/rspb.2020.1405
pmc: PMC7575516
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Systematic Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
20201405Subventions
Organisme : NIAID NIH HHS
ID : R01 AI138783
Pays : United States
Organisme : Medical Research Council
ID : MC_PC_19012
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 210758/Z/18/Z
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/V009761/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/V038613/1
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/R015600/1
Pays : United Kingdom
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