COVID-19 scenario modelling for the mitigation of capacity-dependent deaths in intensive care: computer simulation study

Richard M Wood; Christopher J McWilliams; Matthew J Thomas; Christopher P Bourdeaux; Christos Vasilakis

Computer simulations of patient flow, demand and capacity have been used extensively to inform decision-making in healthcare (Fone et al, 2003 , Griffiths et al, 2013 , Mohiuddin et al, 2017 , Wood & Murch, 2019 . This is especially true for the stochastic, discrete-event approach to simulation, as it is particularly suited to situations where entities (e.g. patients) "compete" for limited resources such as hospital beds and operating room time (Pitt et al, 2016) . Many simulation studies that have tackled questions around demand and capacity in healthcare, both under typical health system conditions (for example Bagust et al, 1999 , Demir et al, 2015 and in periods of increased pressure such as mass casualty events (Glasgow et al, 2018) and winter bed crises (Vasilakis & El-Darzi, 2001 , Wood, 2019 . Specifically in the context of intensive care, simulation studies have addressed bed requirements by using the system dynamics simulation approach to evaluate different management policies (Mahmoudian-Dehkrodi & Sadat, 2016) , and applying analytical queuing models and simulations to the management of patient flow (Kim et al, 1999 , Griffiths et al, 2010 . This paper reports on the development and application of a purpose-built computer simulation model and accompanying easy-to-use open source software, designed for evaluating scenarios to mitigate capacity-dependent deaths from a COVID-19 (or other) pandemic. It should be noted that capacityindependent deaths, occurring when patients are cared for in the most appropriate setting, are out of scope of this study. The remainder of this paper is structured as follows. Development of the model and implementation is covered in Section 2 alongside data requirements for model parameterisation and the scenarios considered for experimenting with the model. Illustrative results, on application to an intensive care unit in a large teaching hospital in England, are presented in Section 3. Finally, Section 4 contains a discussion on limitations, practical application, and further uses of the model and tool.