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

该奖项将有助于我们理解在资源受限的环境中有效应对全国范围内的阿片类药物危机的公共安全对策，从而促进国家的健康、繁荣和福利。根据疾控中心的数据，自1999年以来，药物过量死亡人数增加了四倍多，2017年导致美国超过7万人死亡，其中近70%与阿片类药物有关。美国社区付出了巨大的经济和社会代价。对于警察机构来说，这些成本尤其严重。最近，一些警察部门启动了一些方案，试图将阿片类药物使用者从刑事司法系统转移到治疗中。注重减少供应网络的需求方需要警察业务模式的转变，需要新的工作流程和对警察的培训。该奖项支持对动态的、数据驱动的警察调度和反应政策的基本理解，这些政策可以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来源的警务和医疗数据整合在一起，作为项目试验台。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。这项教育计划旨在通过K12外展扩大人们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，以扰乱非法毒品供应链，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行动。这一框架将作为研究如何利用和推广随机规划、网络阻断模型和因果推理的最先进技术来扰乱非法阿片网络供需的基础。该项目将制定新的随机规划模型，以研究如何将吸毒者转移到治疗而不是刑事司法系统，并将制定新的分级设施选址游戏和网络拦截模型，以研究如何在多个供应网络上阻断非法阿片类药物的供应。该项目将分析模型特征、有效的模型公式、纳什均衡存在的证明、稳定性的代价界限，以及为解决大型网络游戏中的大规模问题实例和恢复纳什均衡而开发的新算法技术。该项目将为以经济高效的方式利用警察资源减少阿片类药物的使用、扰乱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。

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.