Overton, C.E. orcid.org/0000-0002-8433-4010, Pellis, L., Stage, H.B. orcid.org/0000-0001-9938-8452 et al. (9 more authors) (2022) EpiBeds: Data informed modelling of the COVID-19 hospital burden in England. PLOS Computational Biology, 18 (9). ARTN e1010406. ISSN 1553-734X
Abstract
The first year of the COVID-19 pandemic put considerable strain on healthcare systems worldwide. In order to predict the effect of the local epidemic on hospital capacity in England, we used a variety of data streams to inform the construction and parameterisation of a hospital progression model, EpiBeds, which was coupled to a model of the generalised epidemic. In this model, individuals progress through different pathways (e.g. may recover, die, or progress to intensive care and recover or die) and data from a partially complete patient-pathway line-list was used to provide initial estimates of the mean duration that individuals spend in the different hospital compartments. We then fitted EpiBeds using complete data on hospital occupancy and hospital deaths, enabling estimation of the proportion of individuals that follow the different clinical pathways, the reproduction number of the generalised epidemic, and to make short-term predictions of hospital bed demand. The construction of EpiBeds makes it straightforward to adapt to different patient pathways and settings beyond England. As part of the UK response to the pandemic, EpiBeds provided weekly forecasts to the NHS for hospital bed occupancy and admissions in England, Wales, Scotland, and Northern Ireland at national and regional scales.
Metadata
Item Type: | Article |
---|---|
Authors/Creators: |
|
Editors: |
|
Copyright, Publisher and Additional Information: | © 2022 Overton et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
Keywords: | Humans; Hospitalization; Hospitals; England; Pandemics; COVID-19 |
Dates: |
|
Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Applied Mathematics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 29 Jan 2024 14:28 |
Last Modified: | 29 Jan 2024 14:28 |
Published Version: | https://journals.plos.org/ploscompbiol/article?id=... |
Status: | Published |
Publisher: | Public Library of Science (PLoS) |
Identification Number: | 10.1371/journal.pcbi.1010406 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:208328 |
Download
Filename: EpiBeds Data informed modelling of the COVID-19 hospital burden in England.pdf
Licence: CC-BY 4.0