Establishing an SEIR-based framework for local modelling of COVID-19 infections, hospitalisations and deaths

R. M. Wood, A. C. Pratt, B. J. Murch, A. L. Powell, R. D. Booton, D. G. Thomas, J. Twigger, E. Diakou, S. Coleborn, T. Manning, C. Davies, K. M. Turner

Research output: Contribution to journalArticlepeer-review

2 Citations (SciVal)

Abstract

Without timely assessments of the number of COVID-19 cases requiring hospitalisation, healthcare providers will struggle to ensure an appropriate number of beds are made available. Too few could cause excess deaths while too many could result in additional waits for elective treatment. As well as supporting capacity considerations, reliably projecting future “waves” is important to inform the nature, timing and magnitude of any localised restrictions to reduce transmission. In making the case for locally owned and locally configurable models, this paper details the approach taken by one major healthcare system in founding a multi-disciplinary “Scenario Review Working Group”, comprising commissioners, public health officials and academic epidemiologists. The role of this group, which met weekly during the pandemic, was to define and maintain an evolving library of plausible scenarios to underpin projections obtained through an SEIR-based compartmental model. Outputs have informed decision-making at the system’s major incident Bronze, Silver and Gold Commands. This paper presents illustrated examples of use and offers practical considerations for other healthcare systems that may benefit from such a framework.

Original languageEnglish
Pages (from-to)337-347
Number of pages11
JournalHealth Systems
Volume10
Issue number4
Early online date6 Sep 2021
DOIs
Publication statusPublished - 31 Dec 2021

Keywords

  • compartmental modelling
  • coronavirus
  • COVID-19
  • public health
  • scenario analysis

ASJC Scopus subject areas

  • Health Policy
  • Health Informatics
  • Computer Science Applications
  • Health Information Management

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