A Novel Approach for Outcomes Estimation in Hybrid Simulation Models of Disease Transmission and Progression

In this study, we consider hybrid simulations consisting of an agent-based simulation (ABS) to model disease transmission in a population, and a discrete-time Markov chain executed as a Monte Carlo simulation to model the heterogeneous progression of the disease in infected agents. In such scenarios, execution of the ABS is stopped at a certain time point. At this point, disease-related outcomes for infected agents are estimated by executing the disease progression Monte Carlo simulation for each infected agent over their lifespans, well beyond the execution horizon of the ABS. This can incur substantial computational expense. We present a novel method to alleviate this computational burden by randomly sampling and allocating disease-related outcomes from a repository of outcomes generated and stored as a one-time exercise prior to execution of the hybrid simulation. We demonstrate the effectiveness of our approach via a stylized hybrid simulation of a hypothetical infectious disease transmission scenario.