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

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.

KEYWORDS:

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

Acknowledgments

The authors are grateful to the anonymous reviewers whose helpful and constructive comments have improved the quality and legibility of this article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Author contributions

RMW conceived the need for the Scenario Review Working Group, determined its format and membership, and obtained sponsorship for its foundation. RMW, ACP, BJM and ALP set the agenda for SRWG meetings, led the discussions, and generated the modelled outputs. RDB and KMT led development on the “SEIR” mathematical model. All authors participated in the SRWG by contributing to model and scenario calibration. RMW wrote the manuscript.