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A hospital demand and capacity intervention approach for COVID-19

Van Yperen, James, Campillo-Funollet, Eduard, Inkpen, Rebecca, Memon, Anjum, Madzvamuse, Anotida (2023) A hospital demand and capacity intervention approach for COVID-19. PLoS ONE, 18 (5). Article Number e0283350. ISSN 1932-6203. (doi:10.1371/journal.pone.0283350) (KAR id:101294)


The mathematical interpretation of interventions for the mitigation of epidemics in the literature often involves finding the optimal time to initiate an intervention and/or the use of the number of infections to manage impact. Whilst these methods may work in theory, in order to implement effectively they may require information which is not likely to be available in the midst of an epidemic, or they may require impeccable data about infection levels in the community. In reality, testing and cases data can only be as good as the policy of implementation and the compliance of the individuals, which implies that accurately estimating the levels of infections becomes difficult or complicated from the data that is provided. In this paper, we demonstrate a different approach to the mathematical modelling of interventions, not based on optimality or cases, but based on demand and capacity of hospitals who have to deal with the epidemic on a day to day basis. In particular, we use data-driven modelling to calibrate a susceptible-exposed-infectious-recovered-died type model to infer parameters that depict the dynamics of the epidemic in several regions of the UK. We use the calibrated parameters for forecasting scenarios and understand, given a maximum capacity of hospital healthcare services, how the timing of interventions, severity of interventions, and conditions for the releasing of interventions affect the overall epidemic-picture. We provide an optimisation method to capture when, in terms of healthcare demand, an intervention should be put into place given a maximum capacity on the service. By using an equivalent agent-based approach, we demonstrate uncertainty quantification on the likelihood that capacity is not breached, by how much if it does, and the limit on demand that almost guarantees capacity is not breached.

Item Type: Article
DOI/Identification number: 10.1371/journal.pone.0283350
Uncontrolled keywords: Epidemics, Hospitals, Health Services Needs and Demand, Humans, COVID-19 - epidemiology, Models, Theoretical
Subjects: Q Science > QA Mathematics (inc Computing science)
R Medicine
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Funders: Wellcome Trust (
National Institute for Health Research (
SWORD Depositor: JISC Publications Router
Depositing User: JISC Publications Router
Date Deposited: 23 May 2023 15:08 UTC
Last Modified: 04 Mar 2024 18:56 UTC
Resource URI: (The current URI for this page, for reference purposes)

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