The Library
Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number
Tools
Tools
Tools
Keeling, Matt J., Dyson, Louise, Guyver-Fletcher, Glen, Holmes, Alexander, Semple, Malcolm G., Tildesley, Michael J. and Hill, Edward M. (2022) Fitting to the UK COVID-19 outbreak, short-term forecasts and estimating the reproductive number. Statistical Methods in Medical Research . doi:10.1177/09622802211070257 (In Press)
|
PDF
WRAP-fitting-UK-COVID-19-outbreak-short-term-forecasts-estimating-reproductive-number-2021.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (2574Kb) | Preview |
Official URL: http://dx.doi.org/10.1177/09622802211070257
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, R, has taken on special significance in terms of the general understanding of whether the epidemic is under control (R<1). 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: | Journal Article | |||||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Subjects: | Q Science > QA Mathematics Q Science > QR Microbiology R Medicine > RA Public aspects of medicine |
|||||||||||||||||||||||||||||||||
| Divisions: | Faculty of Science > Life Sciences (2010- ) Faculty of Science > Mathematics |
|||||||||||||||||||||||||||||||||
| Library of Congress Subject Headings (LCSH): | COVID-19 Pandemic, 2020- -- Great Britain, COVID-19 (Disease) , COVID-19 (Disease) -- Great Britain -- Evaluation -- Statistics , COVID-19 (Disease) -- Diagnosis -- Great Britain, COVID-19 (Disease) -- Epidemiology -- Mathematical models, COVID-19 (Disease) -- Forecasting -- Mathematical models, COVID-19 (Disease) -- Transmission -- Statistical methods, Viruses -- Reproduction -- Mathematical models, Markov processes , Monte Carlo method , Bayesian statistical decision theory | |||||||||||||||||||||||||||||||||
| Journal or Publication Title: | Statistical Methods in Medical Research | |||||||||||||||||||||||||||||||||
| Publisher: | Sage Publications Ltd. | |||||||||||||||||||||||||||||||||
| ISSN: | 0962-2802 | |||||||||||||||||||||||||||||||||
| Official Date: | 17 January 2022 | |||||||||||||||||||||||||||||||||
| Dates: |
|
|||||||||||||||||||||||||||||||||
| DOI: | 10.1177/09622802211070257 | |||||||||||||||||||||||||||||||||
| Status: | Peer Reviewed | |||||||||||||||||||||||||||||||||
| Publication Status: | In Press | |||||||||||||||||||||||||||||||||
| Access rights to Published version: | Open Access | |||||||||||||||||||||||||||||||||
| RIOXX Funder/Project Grant: |
|
|||||||||||||||||||||||||||||||||
| Contributors: |
|
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
Downloads
Downloads per month over past year
