Incorporating the dynamics of epidemics in simulation models of healthcare systems

A growing concern among healthcare planners is the ability to determine the capacity of medical resources in various potential situations and prepare accordingly. In this study, we consider uncertain situations such as epidemic diseases that could affect the patient flow in a healthcare system by developing a discrete-event simulation model for a local community health clinic in Lubbock, Texas. Conventionally, in these healthcare system models, patients are assumed to arrive at the system based on a schedule, along with some random entries to represent walk-in patients; we propose an additional level of uncertainty based on the dynamics of an epidemic. After developing the simulation model for the baseline probability of the clinic, we simulate the susceptible-infected-recovery (SIR) process to generate epidemic patients for the model developed for the clinic. Ideally, this shows how epidemic diseases could affect the flow of patients which in turn affects the performance of the clinic. We examine eleven epidemic scenarios with different levels of disease outbreak. Our performance measures include the conditional expected value of the length of stay (LoS) of patients and the system throughput. The incorporated model reveals how the various epidemic scenarios affect the performance measures of the clinic. Moreover, based on the statistics of LoS and system throughput, we examine different alternative clinic designs for each epidemic scenario. We consider two views of analyzing the alternative designs, scenario-oriented and design-oriented views, to obtain the six best alternatives and evaluate the costs and benefits of each to find the best design.