ISN2: Disrupting Opioid Supply Chains through Policing: A Mathematical Modeling Framework

该奖项将促进我们了解在资源有限的环境中对全国性阿片类药物危机作出有效的公共安全反应，从而加强国家健康、繁荣和福利。根据疾病预防控制中心的数据，自1999年以来，药物过量死亡人数增加了两倍多，导致2017年美国有7万多人死亡，其中近70%的死亡与阿片类药物有关。美国社区为此付出了巨大的经济和社会代价。这些成本对警察机构来说尤其严重。最近，一些警察部门启动了一些项目，试图将阿片类药物使用者从刑事司法系统转移到治疗中。专注于减少供应网络的需求方，需要警察行动的范式转变，需要新的工作流程和对警察的培训。该奖项支持对动态、数据驱动的警察调度和响应政策的基本理解，从而更有效地利用有限的警察资源来帮助支持社区卫生。该项目使用独特的数据集，将来自威斯康星州戴恩县的社区警务和医疗数据整合在一起，作为项目的试验台。这项研究将与当地执法机构、公共安全专家和医生合作进行。该教育计划旨在通过K12外展扩大对工程的兴趣和参与。这项研究将制定一个数学建模框架，以制定创新的、动态的警察时间表和应对政策，扰乱非法药物供应链，有效利用有限的警察资源，并为阿片类药物使用者和社区带来更好的健康结果。该框架将捕捉阿片类药物使用者和警察在社区中的移动情况。该框架将作为研究如何通过利用和扩展来自随机规划、网络拦截模型和因果推理的最先进技术来破坏非法阿片类药物网络供应和需求的基础。该项目将制定新的随机规划模型，用于研究如何将吸毒者转移到治疗而不是刑事司法系统，以及新的分层设施位置游戏和网络拦截模型，用于研究如何在多个供应网络中拦截非法阿片类药物的供应。该项目将产生模型特征的分析，有效的模型公式，纳什均衡存在的证明，稳定价格的界限，以及解决大规模问题实例和恢复大规模网络博弈中的纳什均衡的新算法技术的发展。该项目将为经济有效地利用警察资源减少阿片类药物使用、破坏非法阿片类药物供应链和改善社区健康提供新的见解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award will enhance the national health, prosperity, and welfare by contributing to our understanding of an effective public safety response to the nationwide opioid crisis in resource-constrained environments. According to the CDC, drug overdose deaths have more than quadrupled since 1999, resulting in more than 70,000 deaths in the US in 2017, with nearly 70 percent of these deaths related to opioids. The economic and social costs to US communities have been enormous. These costs are particularly acute for police agencies. Recently, some police departments have initiated programs that seek to divert opioid users from the criminal justice system to treatment. Focus on reducing the demand-side of the supply network necessitates a paradigm shift in police operations, requiring new workflows and training for police officers. This award supports a fundamental understanding of dynamic, data-driven police scheduling and response policies that more efficiently use limited police resources to help support community health. The project uses unique data sets that integrate community-sourced policing and medical data from Dane County, Wisconsin, which serves as a project test bed. The research will be performed in collaboration with local law enforcement agencies, public safety experts, and medical doctors. The educational plan aims to broaden interest and participation in engineering through K12 outreach.This research will formulate a mathematical modeling framework to prescribe innovative, dynamic police schedules and response policies that disrupt illicit drug supply chains, efficiently use limited police resources, and lead to better health outcomes for opioid users and the community. The framework will capture the movement of opioid users and police officers in a community. This framework will serve as the basis for studying how to disrupt the supply and demand of illicit opioid networks by leveraging and extending state-of-the-art techniques from stochastic programming, network interdiction models, and causal inference. This project will formulate new stochastic programming models for studying how to divert drug users to treatment rather than the criminal justice system as well as new hierarchical facility location games and network interdiction models for studying how to interdict the supply of illicit opioids across multiple supply networks. The project will produce an analysis of model features, efficient model formulations, proofs of the existence of Nash equilibria, bounds on the price of stability, and the development of new algorithmic techniques for solving large-scale problem instances and recovering Nash equilibria in large-scale network games. This project will provide new insights for cost-effectively using police resources to reduce opioid usage, disrupt illicit opioid supply chains, and improve community health.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.