Fitting the reproduction number from UK coronavirus case data and why it is close to 1

Graeme J. Ackland*, James A. Ackland, Mario Antonioletti, David J. Wallace

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

We present a method for rapid calculation of coronavirus growth rates and R-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 R-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 R(t) on insight from localized spread models, which show R(t) going asymptotically to 1 after a transient, regardless of how large the R 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.
Original languageEnglish
Pages (from-to)1-26
Number of pages26
JournalPhilosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences
Issue number2233
Early online date15 Aug 2022
Publication statusPublished - 3 Oct 2022


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