Data driven compartmental modelling of the COVID-19 hospital burden in England

Monday, June 14 at 11:30pm (PDT)
Tuesday, June 15 at 07:30am (BST)
Tuesday, June 15 03:30pm (KST)

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Christopher Overton

University of Manchester
"Data driven compartmental modelling of the COVID-19 hospital burden in England"
The COVID-19 pandemic in England has put considerable strain on the national healthcare system. To predict the effect of the pandemic on hospital capacity in England, we used a variety of data streams to inform the construction and parameterisation of a hospital progression model, which was coupled to a model of the generalised epidemic. 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 the model using complete data on hospital occupancy and hospital deaths, enabling estimation of the proportion of individuals that follow different clinical pathways, and the reproduction number (Rt) of the generalised epidemic. The construction of the model makes it straightforward to adapt to different patient pathways and settings beyond England. As part of the UK response to the pandemic, this model has provided weekly forecasts to the NHS for hospital bed occupancy in England, Wales, Scotland and Northern Ireland, and formed part of the UK combined reproduction number estimates.

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