Panovska-Griffiths, J;
Swallow, B;
Hinch, R;
Cohen, J;
Rosenfeld, K;
Stuart, RM;
Ferretti, L;
... Kerr, CC; + view all
(2022)
Statistical and agent-based modelling of the transmissibility of different SARS-CoV-2 variants in England and impact of different interventions.
Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
, 380
(2233)
, Article 20210315. 10.1098/rsta.2021.0315.
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Abstract
The English SARS-CoV-2 epidemic has been affected by the emergence of new viral variants such as B.1.177, Alpha and Delta, and changing restrictions. We used statistical models and the agent-based model Covasim, in June 2021, to estimate B.1.177 to be 20% more transmissible than the wild type, Alpha to be 50-80% more transmissible than B.1.177 and Delta to be 65-90% more transmissible than Alpha. Using these estimates in Covasim (calibrated 1 September 2020 to 20 June 2021), in June 2021, we found that due to the high transmissibility of Delta, resurgence in infections driven by the Delta variant would not be prevented, but would be strongly reduced by delaying the relaxation of restrictions by one month and with continued vaccination. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.
| Type: | Article |
|---|---|
| Title: | Statistical and agent-based modelling of the transmissibility of different SARS-CoV-2 variants in England and impact of different interventions |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1098/rsta.2021.0315 |
| Publisher version: | https://doi.org/10.1098/rsta.2021.0315 |
| Language: | English |
| Additional information: | © 2022 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited. |
| Keywords: | Agent-based modelling, multivariate regression modelling, COVID-19 |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute for Global Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL GOS Institute of Child Health > Population, Policy and Practice Dept |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10160917 |
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