Impact of COVID-19 Epidemics on Bed Requirements in a Healthcare Center Using Data-Driven Discrete-Event Simulation

Abstract

Bed occupancy ratio reflects the state of the hospital at a given time. It is important for management to keep track of this figure to proactively avoid overcrowding and maintain a high level of quality of care. The objective of this work consists in proposing a decision-aid tool for hospital managers allowing to decide on the bed requirements for a given hospital or network of hospitals on a short-medium term horizon. To that extent we propose a new data-driven discrete-event simulation model based on data from a French university hospital to predict bed and staff requirements. We propose a case study to illustrate the tool's ability to monitor bed occupancy in the recovery unit given the admission rate of ED patients during the pandemic of Sars-Cov-2. These results give an interesting insight on the situation, providing decision makers with a powerful tool to establish an enlightened response to this situation.