Hospitalization Forecast to Inform COVID-19 Pandemic Planning and Resource Allocation Using Discrete Event Simulation

Abstract

Purpose: This study aims to address the pressing need for accurate forecasting of healthcare resource demands during the COVID-19 pandemic. It presents an approach that combines a stochastic Markov model and a discrete event simulation model to dynamically predict hospital admissions and daily occupancy of hospital and ICU beds. Design/methodology/approach: The research builds upon existing work related to predicting COVID-19 spread and patient influx to hospital emergency departments. The proposed model was developed and validated at San Juan de Alicante University Hospital from July 10, 2020, to January 10, 2022, and externally validated at Hospital Vega Baja. The model involves an admissions generator based on a stochastic Markov model, feeding data into a discrete event simulation model in the R programming language. The probabilities of hospital admission were calculated based on age-stratified positive SARS-COV-2 results from the health department’s catchment population. The discrete event simulation model simulates distinct patient pathways within the hospital to estimate bed occupancy for the upcoming week. The performance of the model was measured using the median absolute difference (MAD) between predicted and actual demand.Findings: When applied to data from San Juan hospital, the admissions generator demonstrated a MAD of 6 admissions/week (interquartile range [IQR] 2-11). The MAD between the model’s predictions and actual bed occupancy was 20 beds/day (IQR 5-43), equivalent to 5% of total hospital beds. For ICU occupancy, the MAD was 4 beds/day (IQR 2-7), constituting 25% of ICU beds. Evaluation with data from Hospital Vega Baja showcased an admissions generator MAD of 2.42 admissions/week (IQR 1.02-7.41).