EpiBeds: Data informed modelling of the COVID-19 hospital burden in England.
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, Ep...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-09-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010406 |
| _version_ | 1798000434107908096 |
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| author | Christopher E Overton Lorenzo Pellis Helena B Stage Francesca Scarabel Joshua Burton Christophe Fraser Ian Hall Thomas A House Chris Jewell Anel Nurtay Filippo Pagani Katrina A Lythgoe |
| author_facet | Christopher E Overton Lorenzo Pellis Helena B Stage Francesca Scarabel Joshua Burton Christophe Fraser Ian Hall Thomas A House Chris Jewell Anel Nurtay Filippo Pagani Katrina A Lythgoe |
| author_sort | Christopher E Overton |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T11:20:11Z |
| format | Article |
| id | doaj.art-14254f9add4d4613988bea8a1292cbaf |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-11T11:20:11Z |
| publishDate | 2022-09-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-14254f9add4d4613988bea8a1292cbaf2022-12-22T04:27:06ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-09-01189e101040610.1371/journal.pcbi.1010406EpiBeds: Data informed modelling of the COVID-19 hospital burden in England.Christopher E OvertonLorenzo PellisHelena B StageFrancesca ScarabelJoshua BurtonChristophe FraserIan HallThomas A HouseChris JewellAnel NurtayFilippo PaganiKatrina A LythgoeThe 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.https://doi.org/10.1371/journal.pcbi.1010406 |
| spellingShingle | Christopher E Overton Lorenzo Pellis Helena B Stage Francesca Scarabel Joshua Burton Christophe Fraser Ian Hall Thomas A House Chris Jewell Anel Nurtay Filippo Pagani Katrina A Lythgoe EpiBeds: Data informed modelling of the COVID-19 hospital burden in England. PLoS Computational Biology |
| title | EpiBeds: Data informed modelling of the COVID-19 hospital burden in England. |
| title_full | EpiBeds: Data informed modelling of the COVID-19 hospital burden in England. |
| title_fullStr | EpiBeds: Data informed modelling of the COVID-19 hospital burden in England. |
| title_full_unstemmed | EpiBeds: Data informed modelling of the COVID-19 hospital burden in England. |
| title_short | EpiBeds: Data informed modelling of the COVID-19 hospital burden in England. |
| title_sort | epibeds data informed modelling of the covid 19 hospital burden in england |
| url | https://doi.org/10.1371/journal.pcbi.1010406 |
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