Application of Distributed Agent-based Modeling to Investigate Opioid Use Outcomes in Justice Involved Populations.

Eric Tatara, John Schneider, Madeline Quasebarth, Nicholson Collier, Harold Pollack, Basmattee Boodram, Sam Friedman, Elizabeth Salisbury-Afshar, Mary Ellen Mackesy-Amiti, Jonathan Ozik
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Abstract

Criminal justice involved (CJI) individuals with a history of opioid use disorder (OUD) are at high risk of overdose and death in the weeks following release from jail. We developed the Justice-Community Circulation Model (JCCM) to investigate OUD/CJI dynamics post-release and the effects of interventions on overdose deaths. The JCCM uses a synthetic agent-based model population of approximately 150,000 unique individuals that is generated using demographic information collected from multiple Chicago-area studies and data sets. We use a high-performance computing (HPC) workflow to implement a sequential approximate Bayesian computation algorithm for calibrating the JCCM. The calibration results in the simulated joint posterior distribution of the JCCM input parameters. The calibrated model is used to investigate the effects of a naloxone intervention for a mass jail release. The simulation results show the degree to which a targeted intervention focusing on recently released jail inmates can help reduce the risk of death from opioid overdose.

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应用基于分布式代理的建模来调查司法介入人群中阿片类药物的使用结果。
有阿片类药物使用障碍(OUD)病史的刑事司法(CJI)涉案人员在出狱后的几周内面临用药过量和死亡的高风险。我们开发了司法-社区循环模型 (JCCM),以研究释放后 OUD/CJI 的动态以及干预措施对用药过量死亡的影响。JCCM 使用基于合成代理的模型人口,该人口约有 150,000 个独特的个体,是利用从芝加哥地区多项研究和数据集收集的人口信息生成的。我们使用高性能计算 (HPC) 工作流程来实施一种用于校准 JCCM 的顺序近似贝叶斯计算算法。校准结果是模拟 JCCM 输入参数的联合后验分布。校准后的模型用于研究纳洛酮干预对大规模监狱释放的影响。模拟结果表明,针对近期出狱的囚犯采取有针对性的干预措施在多大程度上有助于降低阿片类药物过量致死的风险。
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