Risk Assessment of Emergency Medical Resource Exhaustion Under Major Infectious Disease Outbreaks: Based on Discrete-Event Simulation Models

Objective: To quantify the risk of emergency medical resource exhaustion during the early stages of major infectious disease outbreaks and proposed resource allocation optimization strategies. Methods: This study integrated discrete event simulation and patient flow theory to develop a dynamic medical resource allocation simulation system in Hubei Province from January 23 to February 21, 2020. This period referred to the first 30 days interval following the implementation of the lockdown in Wuhan. The system simulated the utilization of general hospital beds, ICU beds, and ventilators under three distinct resource supply scenarios: baseline (Expected), optimal (Best case), and worst (Worst case). Simulation outputs including cumulative depletion days, waiting time, and deaths attributable to delayed access to critical resources were summarized using descriptive statistical analysis. Results: In the Best case scenario, the cumulative depletion days of ICU beds and ventilators persisted for 4 days and 6 days respectively, while no shortages occurred for general beds; 384 deaths were attributable to waiting for resources. In the Expected scenario, the cumulative depletion days of ICU beds, ventilators, and general beds were 11 days, 17 days, and 1 day respectively, with 766 deaths attributable to waiting for resources. In the Worst case scenario, the cumulative depletion days of both ICU beds and ventilators were 28 days, and general beds also experienced severe congestion resulting in an average wait time of 4.18 days, a maximum wait time of 11.28 days, and with 15,029 deaths attributable to waiting for resources. Conclusion: The abrupt surge in cases at the onset of the epidemic exerted considerable pressure on medical resources. The intensity of resource supply is highly correlated with the risk of patient death, and ventilators and ICU beds are the key resources affecting the death risk. This simulation model can provide a scientific tool for emergency resource reserve and allocation in public health emergencies.