OPOSim: An Agent-Based Model for Evaluating Long-Term Effects of Opioid Policy Outcomes in North Carolina

Michael A. Duprey (), Joëlla W. Adams (), Sazid S. Khan () and Georgiy V. Bobashev ()

Journal of Artificial Societies and Social Simulation, 2026, vol. 29, issue 1, 5

Abstract: The opioid epidemic remains a critical public health issue in the United States, with North Carolina experiencing particularly high opioid-related mortality rates. This study introduces OPOSim, an agent-based model designed to assess the long-term impacts of opioid policy interventions. By incorporating social networks and simulating opioid use dynamics, we use OPOSim to evaluate the effects of prevention strategies, specifically targeting the reduction of transitions from prescribed opioids and non-opioid substance use to heroin/synthetic opioid use disorder within North Carolina. The results demonstrate a significant delay between intervention implementation and observable mortality reductions, emphasizing the need for long-term planning. Even partial reductions in transition rates can notably decrease opioid-related deaths over time. OPOSim offers a valuable tool for understanding the opioid crisis and informing policy decisions, providing insights into the effectiveness of various interventions to mitigate the epidemic in North Carolina and similar settings.

Keywords: Agent-Based Modeling; Social Networks; Opioid Epidemic; Opioid Use Disorder; Prevention Strategies; Public Health Policy (search for similar items in EconPapers)
Date: 2026-01-31
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Persistent link: https://EconPapers.repec.org/RePEc:jas:jasssj:2024-84-3

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