A Framework for Efficient Individual-Level Longitudinal Outcomes Estimation for Agent-Based Simulations

In this article, we consider an agent-based modeling situation where individual-level stochastic outcomes for a subset of agents have to be estimated over time. This situation is commonly found in agent-based models of infectious disease transmission, where outcomes such as life years survived are accumulated over time as a stochastic process. For an ABM with a large agent cohort, this longitudinal outcome estimation process can incur substantial computational expense. We present two methods to alleviate this expense, both of which do not involve any curve-fitting; however, they involve one-time generation of repositories of outcome realizations stratified based on agent characteristics. The first method, the outcomes accumulation and allocation (OAA) approach, involves accumulation of outcomes between relevant changes in agent status as well as random sampling and allocation of outcomes from the repository at relevant points of change in agent status. The second method, the sample path-based sampling and allocation (SPSA) approach, eliminates the need for outcomes accumulation within the ABM execution by allocating outcomes based on the sample path experienced by an agent. Computational experiments with an ABM of the spread of hepatitis C virus in the Indian context indicate that runtimes under the OAA and SPSA approaches are approximately three-fifth and one-third of the benchmark accumulation-only outcomes estimation approach, while yielding comparable outcome distributions and their expectations. We also provide theoretical support for equivalence of outcome expectations between the OAA, SPSA and the benchmark accumulation-only approaches, and discuss the worst-case space complexity of repository generation for each method.