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Statistical and agent-based modelling of the transmissibility of different SARS-CoV-2 variants in England and impact of different interventions.
Panovska-Griffiths, J; Swallow, B; Hinch, R; Cohen, J; Rosenfeld, K; Stuart, R M; Ferretti, L; Di Lauro, F; Wymant, C; Izzo, A; Waites, W; Viner, R; Bonell, C; Fraser, C; Klein, D; Kerr, C C.
Affiliation
  • Panovska-Griffiths J; The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford.
  • Swallow B; The Queen's College, University of Oxford, Oxford.
  • Hinch R; School of Mathematics and Statistics, University of Glasgow, Glasgow, UK.
  • Cohen J; The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford.
  • Rosenfeld K; Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, WA, USA.
  • Stuart RM; Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, WA, USA.
  • Ferretti L; University of Copenhagen, Copenhagen, Denmark.
  • Di Lauro F; The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford.
  • Wymant C; The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford.
  • Izzo A; The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford.
  • Waites W; Institute for Disease Modeling, Bill and Melinda Gates Foundation, Seattle, WA, USA.
  • Viner R; Department of Public Health, Environments & Society, London School of Hygiene and Tropical Medicine, London, UK.
  • Bonell C; Department of Computer and Information Sciences, University of Strathclyde, G1 1XH Glasgow, UK.
  • Fraser C; UCL Great Ormond St. Institute of Child Health, University College London, London, UK.
  • Klein D; Department of Public Health, Environments & Society, London School of Hygiene and Tropical Medicine, London, UK.
  • Kerr CC; The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford.
Philos Trans A Math Phys Eng Sci ; 380(2233): 20210315, 2022 Oct 03.
Article in En | MEDLINE | ID: mdl-35965458
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'.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Risk_factors_studies Limits: Humans Language: En Journal: Philos Trans A Math Phys Eng Sci Journal subject: BIOFISICA / ENGENHARIA BIOMEDICA Year: 2022 Document type: Article Country of publication: United kingdom
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: SARS-CoV-2 / COVID-19 Type of study: Risk_factors_studies Limits: Humans Language: En Journal: Philos Trans A Math Phys Eng Sci Journal subject: BIOFISICA / ENGENHARIA BIOMEDICA Year: 2022 Document type: Article Country of publication: United kingdom