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Modelling ICU capacity and access considerations during periods of intense demand

A series of interconnected empirical modelling and simulation studies carried out at the peak of the Covid-19 pandemic

Project status

In progress


Project started on 15 Mar 2020

We conducted a series of interconnected empirical modelling and simulation studies carried out at the peak of the Covid-19 pandemic.

Study 1: Exploring how 'capacity dependent' Covid-19 deaths can be mitigated

Managing healthcare demand and capacity is especially difficult in the context of the COVID-19 pandemic, where limited intensive care resources can be overwhelmed by a large number of cases requiring admission in a short space of time. If patients are unable to access this specialist resource, then death is a likely outcome.

In appreciating these ‘capacity-dependent’ deaths, we developed a stochastic discrete event simulation model designed to capture the key dynamics of the intensive care admissions process for COVID-19 patients.

With application to a large public hospital in England during an early stage of the pandemic, the purpose of this study was to estimate the extent to which such capacity-dependent deaths can be mitigated through demand-side initiatives involving non-pharmaceutical interventions and supply-side measures to increase surge capacity.

Based on information available at the time, modelling results suggested that total capacity-dependent deaths can be reduced by 75% through a combination of increasing capacity from 45 to 100 beds, reducing length of stay by 25%, and flattening the peak demand to 26 admissions per day.

The main paper of this very early COVID-19 study was published in Health Care Management Science in Jul 2020 (submitted in April 2020).

Study 2: The value of triage during periods of intense COVID-19 demand

We expanded the model developed in the first study to include notions of prioritising access to ICU during periods of intense COVID-19 demand.

We found that improved results can be achieved by admitting all patients but considering early discharge for those with worse survival chances given arrival of others with better prognoses and lack of spare capacity. Under this “reverse triage” strategy, total life-years lost can be reduced by 12%, based on a conservative assessment of the likelihood of death following early discharge.

The main paper of this project appeared in the journal Medical Decision Making in early 2021.

Read the HDRUK press release: New model finds triaging COVID-19 patients for intensive care can reduce “life-years lost” by at least 12 per cent

Study 3: An Algorithmic Model for Critical Medical Resource Rationing in a Public Health Emergency

The aim of this project is to develop an algorithmic model that calibrates a dynamic index for patient priority by addressing the shortcomings of the current allocation protocols of scarce medical resources.

The project is funded by a UKRI grant and is in collaboration with researchers from Durham University (Assoc Prof Li Ding), Loughborough University (Dr Dong Li) and Bayes Business School (Dr Navid Izady).