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.
