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

TL;DR

EpiBeds is a data-informed model that predicts hospital burden during COVID-19 in England by integrating epidemic and hospital data, aiding healthcare capacity planning.

Contribution

The paper introduces EpiBeds, a flexible model combining epidemic dynamics with hospital progression, adaptable to different settings and providing weekly forecasts for NHS capacity.

Findings

EpiBeds accurately predicted hospital occupancy and admissions.

The model estimated clinical pathway proportions and reproduction number.

It demonstrated adaptability across UK regions.

Abstract

The first year of the COVID-19 pandemic put considerable strain on the national healthcare system in England. 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, which was coupled to a model of the generalised epidemic. We named this model EpiBeds. 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 different clinical pathways, and the reproduction number of the generalised epidemic. 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 has provided weekly forecasts to the NHS for hospital bed occupancy and admissions in England, Wales, Scotland, and Northern Ireland.