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

Abstract

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. This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.

Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations

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“…2), as well as many less celebrated and more specific graphical devices [ 84 ]. Idioms also involve approaches to generating composite graphics, for example through Small Multiples [ 8 ] (fig. 7), [ 7 ] (fig.…”

Tracking the national and regional COVID-19 epidemic status in the UK using weighted principal component analysis

“…Elsewhere in this issue, alternative approaches have been proposed for calculation or from cases only [ 16 , 17 ]; however, our approach utilizes all available data streams and is also able to find additional second-order structures in the data.…”

Estimation of age-stratified contact rates during the COVID-19 pandemic using a novel inference algorithm

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