HIV case management using agent-based modeling approach subject to antiretroviral therapy and lifestyle treatment plan

Abstract

This study presents a spatially explicit stochastic agent-based model (ABM) to simulate the complex non-linear interactions between T-cells, HIV virions, and therapeutic agents. The model framework operates on a two-dimensional grid, explicitly modeling biological entities as discrete agents to investigate the effects of antiretroviral drug therapy and specific lifestyle interventions. To benchmark model behavior, three distinct clinical stages of HIV infection were simulated under four comparative intervention scenarios. Quantification across 100 independent stochastic replicates per scenario demonstrated robust findings. The key result is that while antiretroviral drug therapy is essential for immediate viral suppression, the combination of drug therapy and lifestyle factors consistently produces a synergistic effect that leads to favorable long-term T-cell outcomes across all clinical stages simulated. Sensitivity analysis employed to assess the model's robustness identified the rate of wild-type virus production as the single most critical biological factor for viral control and clarified that the observed synergy stems mechanistically from lifestyle factors that boost T-cell proliferation, thereby improving immune recovery and promoting the clearance of infected T-cells. The comprehensive analysis in this study presents that a holistic management approach, integrating pharmacological and lifestyle interventions, provides a crucial synergistic effect that promotes sustained immune health. The model presented in this study serves as an exploratory framework for investigating optimal and personalized HIV treatment strategies.