Repository logo
 

EpiBeds: Data informed modelling of the COVID-19 hospital burden in England.

Published version
Peer-reviewed

Type

Article

Change log

Authors

Overton, Christopher E  ORCID logo  https://orcid.org/0000-0002-8433-4010
Pellis, Lorenzo 
Scarabel, Francesca  ORCID logo  https://orcid.org/0000-0003-0250-4555

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.

Description

Funder: Li Ka Shing Foundation


Funder: National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Emergency Preparedness and Response


Funder: National Institute for Health Research Policy Research Programme in Operational Research


Funder: National Institute for Health Research (NIHR)

Keywords

COVID-19, England, Hospitalization, Hospitals, Humans, Pandemics

Journal Title

PLoS Comput Biol

Conference Name

Journal ISSN

1553-734X
1553-7358

Volume Title

18

Publisher

Public Library of Science (PLoS)
Sponsorship
Wellcome Trust (202562/Z/16/Z, 219992/Z/19/Z, 202562/Z/16/Z, 107652/Z/15/Z, UNS73114)
Royal Society (INF\R2\180067)
Medical Research Council (MR/V038613/1)
UKRI (MR/V038613/1)
Wellcome Trust and the Royal Society (202562/Z/16/Z, 107652/Z/15/Z)
Engineering and Physical Sciences Research Council (EP/W011840/1, EP/R018561/1)
NINDS NIH HHS (R21 NS073114)