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