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Fitting the reproduction number from UK coronavirus case data and why it is close to 1.
Ackland, Graeme J; Ackland, James A; Antonioletti, Mario; Wallace, David J.
  • Ackland GJ; School of Physics and Astronomy, University of Edinburgh, Edinburgh EH9 3FD, UK.
  • Ackland JA; Department of Psychology, University of Cambridge,Cambridge CB2 3EB, UK.
  • Antonioletti M; EPCC, University of Edinburgh, Edinburgh EH9 3FD, UK.
  • Wallace DJ; University of St Andrews, St Andrews, Fife, UK.
Philos Trans A Math Phys Eng Sci ; 380(2233): 20210301, 2022 Oct 03.
Article in English | MEDLINE | ID: covidwho-1992459
ABSTRACT
We present a method for rapid calculation of coronavirus growth rates and [Formula see text]-numbers tailored to publicly available UK data. We assume that the case data comprise a smooth, underlying trend which is differentiable, plus systematic errors and a non-differentiable noise term, and use bespoke data processing to remove systematic errors and noise. The approach is designed to prioritize up-to-date estimates. Our method is validated against published consensus [Formula see text]-numbers from the UK government and is shown to produce comparable results two weeks earlier. The case-driven approach is combined with weight-shift-scale methods to monitor trends in the epidemic and for medium-term predictions. Using case-fatality ratios, we create a narrative for trends in the UK epidemic increased infectiousness of the B1.117 (Alpha) variant, and the effectiveness of vaccination in reducing severity of infection. For longer-term future scenarios, we base future [Formula see text] on insight from localized spread models, which show [Formula see text] going asymptotically to 1 after a transient, regardless of how large the [Formula see text] transient is. This accords with short-lived peaks observed in case data. These cannot be explained by a well-mixed model and are suggestive of spread on a localized network. 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: Available Collection: International databases Database: MEDLINE Main subject: Coronavirus / Epidemics Type of study: Observational study / Prognostic study / Systematic review/Meta Analysis Topics: Vaccines / Variants Country/Region as subject: Europa Language: English Journal: Philos Trans A Math Phys Eng Sci Journal subject: Biophysics / Biomedical Engineering Year: 2022 Document Type: Article Affiliation country: Rsta.2021.0301

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Coronavirus / Epidemics Type of study: Observational study / Prognostic study / Systematic review/Meta Analysis Topics: Vaccines / Variants Country/Region as subject: Europa Language: English Journal: Philos Trans A Math Phys Eng Sci Journal subject: Biophysics / Biomedical Engineering Year: 2022 Document Type: Article Affiliation country: Rsta.2021.0301