ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2022
Fitting the reproduction number from UK coronavirus case data and why it is close to 1.
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Combining models to generate consensus medium-term projections of hospital admissions, occupancy and deaths relating to COVID-19 in England.Royal Society open science · 2024Article
- Estimation of local time-varying reproduction numbers in noisy surveillance data.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- The Royal Society RAMP modelling initiative.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Estimation of age-stratified contact rates during the COVID-19 pandemic using a novel inference algorithm.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Heterogeneity in the onwards transmission risk between local and imported cases affects practical estimates of the time-dependent reproduction number.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Tracking the national and regional COVID-19 epidemic status in the UK using weighted principal component analysis.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Fitting the reproduction number from UK coronavirus case data and why it is close to 1.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Modelling herd immunity requirements in Queensland: impact of vaccination effectiveness, hesitancy and variants of SARS-CoV-2.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Technical challenges of modelling real-life epidemics and examples of overcoming these.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
- Visualization for epidemiological modelling: challenges, solutions, reflections and recommendations.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
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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