An Agent-Based Approach for Modeling Disease Control Strategies Using the SIR Model

Understanding infectious disease transmission is essential for reducing pandemic impacts. While previous models have assessed the effects of individual intervention strategies, few have examined their combined impact. This study develops an agent-based model integrating multiple interventions within a traditional SIR framework. This study uses an agent-based SIR model to simulate disease spread and assess control strategies such as face covering and quarantine, represented by five different scenarios. Without interventions, infections peak at 50% of the population. Scenarios show that 10% quarantine compliance reduces peak infections by only 10%, while 50% compliance cuts it by 50%. Preventive strategies significantly reduce this impact; for instance, 50% compliance with masks and quarantine lowers peak infections to under 30% and delays the epidemic peak, providing critical time for healthcare preparedness. These findings guide policymakers in designing adaptive strategies to optimize public health and resources.