D-ISN/Collaborative Research: Mitigating the Harm of Fentanyl through Holistic Demand/Supply Interventions and Equitable Resource Allocations

D-ISN/合作研究：通过整体需求/供应干预和公平资源分配减轻芬太尼的危害

• 批准号: 2240359
• 负责人: Rakesh Nagi
• 金额: $ 51.65万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2027-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2240359&HistoricalAwards=false
• 关键词: ISN; Collaborative; Research; Mitigating; Harm

The objective of this Disrupting Operations of Illicit Supply Networks (D-ISN) research project is to investigate advanced methods and tools to mitigate the harm caused by the distribution and use of illicit opioids in the U.S. The project seeks to reduce the harm caused by illicit opioids by developing methods to study interventions on both the supply-side (by studying the supply chain of opioids) and demand-side (by shifting user pathways towards recovery) and to provide effective resource allocation between these different approaches. The convergent research agenda, augmented with collaboration from multiple community organizations, from courts, law enforcement, to public health, will integrate methods from criminology, public health, and operations research. This research will help to improve our understanding of the operations of illicit opioid supply chains, especially how fentanyl and its analogs find their way into other drugs and how to prevent their flow to users. On the demand-side, the project will enhance understanding of the impact of harm reduction strategies through user pathways to addiction. Overall, the project aims to improve the equitable and optimal allocation of demand- and supply-side intervention resources to mitigate the harm caused by illegal opioid use. The project advances and integrates multidisciplinary methods from product design, production, distribution, graph/network theory, optimization, data science, machine learning, agent-based simulation, and fair allocation to reduce harm caused by fentanyl and illicit opioids. The project has three main thrusts. First, the project investigates supply-Side disruptions through understanding the intersections of fentanyl and other Illicit opioid products and distribution networks. This thrust employs a Bill-Of-Materials perspective on product adulteration, transfer learning between licit and/or Illicit supply chains to fill-in data gaps and understanding and modeling the distribution network with multi-commodity flow disruption models for harm reduction. Secondly, the project studies demand-side interventions for different fentanyl user groups. This thrust comprises an investigation of opioid users’ motivations and pathways to use illicit opioids/fentanyl through semi-structured interviews and surveys, fentanyl dispersion in communities, and agent-based models of drug use and harm reduction strategies. The third thrust develops resource allocation models to study the effects of interventions, law-enforcement, and medication-assisted treatments through simulation modeling. The project will make use of definitions of harm, equity, and fairness in distribution of interventions/resources across socioeconomic groups, and new holistic optimization, and simulation models to bring together demand-side and supply-side decisions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The objective of this Disrupting Operations of Illicit Supply Networks (D-ISN) research project is to investigate advanced methods and tools to mitigate the harm caused by the distribution and use of illicit opioids in the U.S. The project seeks to reduce the harm caused by illicit opioids by developing methods to study interventions on both the supply-side (by studying the supply chain of opioids) and demand-side (by shifting user pathways towards recovery) and to provide Effective resource这些不同方法之间的分配。从法院，执法到公共卫生的多个社区组织的合作进行了融合研究议程，将整合犯罪学，公共卫生和运营研究的方法。这项研究将有助于提高我们对非法阿片类药物供应链的运作的理解，尤其是芬太尼及其类似物如何找到其他药物的方式以及如何防止其流向用户。在需求方面，该项目将通过成瘾的用户途径增强对减少伤害策略的影响的理解。总体而言，该项目旨在改善需求和供应方干预资源的公平和最佳分配，以减轻非法使用阿片类药物造成的伤害。该项目从产品设计，生产，分销，图形/网络理论，优化，数据科学，机器学习，基于代理的模拟和公平分配中推进并整合了多学科方法，以减少芬太尼和非法阿片类药物造成的伤害。该项目有三个主要推力。首先，该项目通过了解芬太尼和其他非法阿片类药物和分销网络的交集来调查供应方中断。这项推力采用了有关产品掺假的材料表观点，在持有的数据间隙和/或非法供应链之间进行转移学习，以填补数据差距，并以多种商品流动流动破坏模型来理解和建模分配网络，以减少损害。其次，该项目研究了针对不同芬太尼用户组的需求侧干预措施。这项推力涉及对阿片类药物使用者的动机和途径进行调查，该途径通过半结构化访谈和调查，社区中的芬太尼分散体以及基于特工的药物使用和减少损害策略的模型，使用非法阿片类药物/芬太尼。第三个推力开发资源分配模型，通过模拟建模研究干预措施，法律执行和药物辅助治疗的影响。该项目将利用对社会经济群体的干预/资源分配的危害，公平和公平性的定义，以及新的整体优化，以及模拟模型，以汇总需求方和供应方决策。该奖项反映了NSF的法定任务，并通过基金会的知识优点和广泛的影响来评估NSF的法定任务，并被视为值得通过评估来进行评估。