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Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - EpiBeds
T2 - Data informed modelling of the COVID-19 hospital burden in England
AU - Overton, Christopher E.
AU - Pellis, Lorenzo
AU - Stage, Helena B.
AU - Scarabel, Francesca
AU - Burton, Joshua
AU - Fraser, Christophe
AU - Hall, Ian
AU - House, Thomas A.
AU - Jewell, Chris
AU - Nurtay, Anel
AU - Pagani, Filippo
AU - Lythgoe, Katrina A.
A2 - Struchiner, Claudio José
PY - 2022/9/6
Y1 - 2022/9/6
N2 - 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.
AB - 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.
KW - Research Article
KW - Medicine and health sciences
KW - People and places
KW - Research and analysis methods
KW - Physical sciences
KW - Biology and life sciences
U2 - 10.1371/journal.pcbi.1010406
DO - 10.1371/journal.pcbi.1010406
M3 - Journal article
VL - 18
JO - PLoS Computational Biology
JF - PLoS Computational Biology
SN - 1553-734X
IS - 9
M1 - e1010406
ER -