Abstract
This paper presents a discrete event simulation model to support decision-making for the short-term planning of hospital resource needs, especially Intensive Care Unit (ICU) beds, to cope with outbreaks, such as the COVID-19 pandemic. Given its purpose as a short-term forecasting tool, the simulation model requires an accurate representation of the current system state and high fidelity in mimicking the system dynamics from that state. The two main components of the simulation model are the stochastic modeling of patient admission and patient flow processes. The patient arrival process is modelled using a Gompertz growth model, which enables the representation of the exponential growth caused by the initial spread of the virus, followed by a period of maximum arrival rate and then a decreasing phase until the wave subsides. We conducted an empirical study concluding that the Gompertz model provides a better fit to pandemic-related data (positive cases and hospitalization numbers) and has superior prediction capacity than other sigmoid models based on Richards, Logistic, and Stannard functions. Patient flow modelling considers different pathways and dynamic length of stay estimation in several healthcare stages using patient-level data. We report on the application of the simulation model in two Autonomous Regions of Spain (Navarre and La Rioja) during the two COVID-19 waves experienced in 2020. The simulation model was employed on a daily basis to inform the regional logistic health care planning team, who programmed the ward and ICU beds based on the resulting predictions.
Highlights
The COVID-19 pandemic presents a major global health threat
Healthcare systems are overburdened as high demand for healthcare services from COVID-19 patients places strains on Intensive Care Units (ICU) capacity and creates excessive workloads for healthcare professionals
We have developed a Discrete Event Simulation (DES) model to predict hospital resource needs, in terms of ward and ICU beds
Summary
Simulation is one of the most suitable analytical tools for the analysis of complex systems, such as healthcare systems, as reflected in numerous specialist articles describing the use of simulation models for decision-making in the healthcare context. Extensions of the classical SIR model (Anastassopoulou et al 2020; Giordano et al 2020; Lin et al 2020; Zhou et al 2020; Casella 2021), as well as stochastic transmission models (Hellewell et al 2020; Kucharski et al 2020) have been developed for the COVID-19 pandemic Such models are complicated and need strong assumptions and simplifications, because they are based on a set of differential equations with initial conditions and a number of adaptive parameters (Xia et al 2009; Li et al 2014; Magal et al 2016; Li and Zhang 2017). The COVID-19 research has produced several papers describing the development of a growth model to predict new cases in countries such as China (Shen 2020), India (Malavika et al 2021), Spain (Sánchez-Villegas and Daponte Codina 2020), and other European countries (Cássaro and Pires 2020) These mathematical models present a set of mathematical equations including adaptive parameters that can be determined numerically based on available real data (Panovska-Griffiths 2020). A simulation model can improve critical resource planning during a pandemic; and can be used as an off-line learning tool to test new triage protocols, which are not always as effective as might be desired, and other hard-to-anticipate factors must be considered
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