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

非法供应网络扰乱行动(D-ISN)研究项目的目的是调查先进的方法和工具，以减轻在美国分发和使用非法阿片类药物造成的危害。该项目寻求通过制定方法来研究供应方(通过研究阿片类药物的供应链)和需求方(通过改变使用者的回收途径)的干预措施，并在这些不同的方法之间提供有效的资源分配，从而减少非法阿片类药物造成的危害。从法院、执法部门到公共卫生等多个社区组织的合作，将整合犯罪学、公共卫生和运筹学的研究方法。这项研究将有助于我们更好地了解非法阿片供应链的运作，特别是芬太尼及其类似物如何进入其他药物以及如何防止其流向用户。在需求方面，该项目将通过用户成瘾途径加强对减少伤害战略的影响的了解。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危害。该项目提出并整合了产品设计、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等多学科方法，以减少芬太尼和非法阿片类药物造成的危害。该项目有三个主要推动力。首先，该项目通过了解芬太尼与其他非法阿片产品和分销网络的交叉来调查供应方的中断。这一努力采用了关于产品掺假的材料清单观点，在合法和/或非法供应链之间转移学习以填补数据缺口，并利用多种商品流动中断模型理解分销网络并对其进行建模，以减少危害。其次，该项目研究了针对不同芬太尼使用者群体的需求方干预措施。这一努力包括通过半结构化访谈和调查调查阿片类药物使用者使用非法阿片类药物/芬太尼的动机和途径，芬太尼在社区中的传播，以及药物使用和减少伤害战略的基于药物的模式。第三个重点是开发资源分配模型，通过模拟建模来研究干预、执法和药物辅助治疗的影响。该项目将利用在社会经济群体中分配干预/资源的损害、公平和公平的定义，以及新的整体优化和模拟模型，将需求方和供应方的决策结合在一起。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.