No abstract

It is possible to develop simulation models that can be used to predict future intensive care unit resource needs.

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.

This paper presents a discrete event simulation model to support the decision-making concerned with the short-term planning of the necessary hospital resources, especially Intensive Care Unit (ICU) beds, to face outbreaks, as the SARS-CoV-2. Being used 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 the admission of new patients and the patient flow through the hospital facilities. For the patient arrival process, we analyze different models based on growth curves of the twenty most affected countries (until June 15) and propose the use of the Gompertz curve. The length of stay is divided into several stages, each one modeled separately. We analyze the starting of the simulation model, which requires different procedures depending on the information available about the patients currently hospitalized. We also report the use of this simulation model during the COVID-19 outbreak in the Autonomous Community of Navarre, in Spain. Every day, the research team informed the regional logistic team in charge of planning the health resources, who programmed the ward and ICU beds based on the resulting predictions.