Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number

Keeling, Matt J, Dyson, Louise, Guyver-Fletcher, Glen, Holmes, Alex, Semple, Malcolm G ORCID: 0000-0001-9700-0418, Tildesley, Michael J and Hill, Edward M ORCID: 0000-0002-2992-2004 (2022) Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number STATISTICAL METHODS IN MEDICAL RESEARCH, 31 (9). pp. 1716-1737. ISSN 0962-2802, 1477-0334

Text 09622802211070257.pdf - Published version Download (2MB) | Preview

Abstract

The COVID-19 pandemic has brought to the fore the need for policy makers to receive timely and ongoing scientific guidance in response to this recently emerged human infectious disease. Fitting mathematical models of infectious disease transmission to the available epidemiological data provide a key statistical tool for understanding the many quantities of interest that are not explicit in the underlying epidemiological data streams. Of these, the effective reproduction number, (Formula presented.), has taken on special significance in terms of the general understanding of whether the epidemic is under control ((Formula presented.)). Unfortunately, none of the epidemiological data streams are designed for modelling, hence assimilating information from multiple (often changing) sources of data is a major challenge that is particularly stark in novel disease outbreaks. Here, focusing on the dynamics of the first wave (March–June 2020), we present in some detail the inference scheme employed for calibrating the Warwick COVID-19 model to the available public health data streams, which span hospitalisations, critical care occupancy, mortality and serological testing. We then perform computational simulations, making use of the acquired parameter posterior distributions, to assess how the accuracy of short-term predictions varied over the time course of the outbreak. To conclude, we compare how refinements to data streams and model structure impact estimates of epidemiological measures, including the estimated growth rate and daily incidence.

Item Type:	Article
Uncontrolled Keywords:	COVID-19, severe acute respiratory syndrome coronavirus 2, mathematical modelling, Markov chain Monte Carlo, Bayesian inference, epidemiology, growth rate, reproduction number, short-term forecasts
Divisions:	Faculty of Health & Life Sciences Faculty of Health & Life Sciences > Inst. Infection, Vet & Ecological Sciences Faculty of Health & Life Sciences > Inst. Life Courses & Medical Sciences Faculty of Health & Life Sciences > Inst. Systems, Molec & Integrative Biology > Inst. Systems, Molec & Integrative Biology
Depositing User:	Symplectic Admin
Date Deposited:	11 Feb 2022 10:28
Last Modified:	16 Jun 2026 10:48
DOI:	10.1177/09622802211070257
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3148751
Disclaimer:	The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate.