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Modelling optimal vaccination strategy for SARS-CoV-2 in the UK

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Moore, Sam, Hill, Edward M., Dyson, Louise, Tildesley, Michael J. and Keeling, Matt J. (2021) Modelling optimal vaccination strategy for SARS-CoV-2 in the UK. PLoS Computational Biology, 17 (5). e1008849. doi:10.1371/journal.pcbi.1008849 ISSN 1553-7358.

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Official URL: https://doi.org/10.1371/journal.pcbi.1008849

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Abstract

The COVID-19 outbreak has highlighted our vulnerability to novel infections.

Faced with this threat and no effective treatment, in line with many other countries, the UK adopted enforced social distancing (lockdown) to reduce transmission—successfully reducing the reproductive number R below one. However, given the large pool of susceptible individuals that remain, complete relaxation of controls is likely to generate a substantial further outbreak. Vaccination remains the only foreseeable means of both containing the infection and returning to normal interactions and behaviour. Here, we consider the optimal targeting of vaccination within the UK, with the aim of minimising future deaths or quality adjusted life year (QALY) losses. We show that, for a range of assumptions on the action and efficacy of the vaccine, targeting older age groups first is optimal and may be sufficient to stem the epidemic if the vaccine prevents transmission as well as disease.

Item Type: Journal Article
Subjects: R Medicine > RA Public aspects of medicine
R Medicine > RM Therapeutics. Pharmacology
Divisions: Faculty of Science, Engineering and Medicine > Science > Mathematics
Library of Congress Subject Headings (LCSH): COVID-19 Pandemic, 2020- , COVID-19 Pandemic, 2020- -- Great Britain, COVID-19 (Disease), COVID-19 (Disease) -- Prevention, COVID-19 (Disease) -- Vaccination , COVID-19 (Disease) -- Prevention -- Mathematical models
Journal or Publication Title: PLoS Computational Biology
Publisher: Public Library of Science
ISSN: 1553-7358
Book Title: Modelling optimal vaccination strategy for SARS-CoV-2 in the UK
Official Date: 2021
Dates:
DateEvent
2021Published
6 May 2021Available
3 March 2021Accepted
Volume: 17
Number: 5
Article Number: e1008849
DOI: 10.1371/journal.pcbi.1008849
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 5 March 2021
Date of first compliant Open Access: 5 March 2021
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
NIHR200411National Institute for Health Researchhttp://dx.doi.org/10.13039/501100000272
EP/S022244/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
MR/V009761/1[MRC] Medical Research Councilhttp://dx.doi.org/10.13039/501100000265
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