Journal article
EpiBeds: Data informed modelling of the COVID-19 hospital burden in England
- 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.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.7MB, Terms of use)
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- Publisher copy:
- 10.1371/journal.pcbi.1010406
Authors
- Publisher:
- Public Library of Science
- Journal:
- PLoS Computational Biology More from this journal
- Volume:
- 18
- Issue:
- 9
- Article number:
- e1010406
- Place of publication:
- United States
- Publication date:
- 2022-09-06
- Acceptance date:
- 2022-07-18
- DOI:
- EISSN:
-
1553-7358
- ISSN:
-
1553-734X
- Pmid:
-
36067224
- Language:
-
English
- Keywords:
- Pubs id:
-
1279433
- Local pid:
-
pubs:1279433
- Deposit date:
-
2023-02-08
- ARK identifier:
Terms of use
- Copyright holder:
- Overton et al
- Copyright date:
- 2022
- Rights statement:
- © 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.
- Licence:
- CC Attribution (CC BY)
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