D-ISN/Collaborative Research: Early Warning Systems for Emerging Epidemics of Illicit Substances

该赠款的目的是开发一个数据驱动的分析框架，以支持新出现的非法药物使用危机的早期预警系统。 阿片类药物过量流行病已经演变为三个确定的阶段，从处方阿片类药物滥用的增加开始，到海洛因过量的快速增加，再到合成阿片类药物（主要是芬太尼的变体）与海洛因，可卡因和假药的结合。 每个阶段都有不同的地理空间和时间特征，涉及犯罪活动和公共卫生模式。 该项目的重点是通过监测和分析多模式数据，及早查明新出现的威胁，如目前日益严重的兽用镇静剂流行病，以便了解根本的因果因素，并制定有效的应对战略。 这项研究采取了一种全面、多学科、以系统为重点的方法，以增进对社区非法药物使用模式的基本了解，这有助于支持供需双方采取多管齐下的有效对策。 该项目涉及来自运筹学、刑事司法和公共卫生政策的PI，并与负责预防贩毒的几个区域机构合作。该项目将让研究生参与并准备开发新的分析工具，以应对复杂的社会挑战。该项目探索了一种新颖的EWS框架，具有变革性的学习和优化方法，用于识别和应对新出现的非法物质威胁。 该项目将收集和利用各种来源的观测数据，以建立预测性和规范性模型。特别是，该项目将（1）开发一种新的地理空间感知预测模型，以通过利用数据中固有的地理空间联系来检测新出现的非法药物威胁并确定高风险社区，（2）通过有效的算法来了解因果路径，以揭示社区中新出现的威胁的驱动因素，（3）优化动态干预策略，以适应不断变化的流行病的新数据，（4）开发决策支持工具，作为拟议的EWS框架的概念验证。预测建模和决策分析框架可推广到其他应用领域的EWS。该多学科团队将与国家和地区药物控制计划合作，以展示拟议的数据驱动的EWS框架的实际影响。该奖项反映了NSF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。

The objective of this Disrupting Operations of Illicit Supply Networks (D-ISN) grant is to develop a data-driven analytical framework to support an Early Warning System (EWS) for emerging illicit substance use crises. The opioid overdose epidemic has evolved in three identified phases, beginning with a rise in presciption opioid abuse, to a rapid increase in heroin overdoses, to synthetic opioids (primarily variants of fentanyl) in combination with heroin, cocaine, and counterfeit pills. Each phase has distinct geospatial and temporal signatures, involving both criminal activity and public health patterns. This project is focused on early identification of new emerging threats, such as the current growing veterinary tranquilizer epidemic, through monitoring and analyzing multimodal data in order to understand underlying causal factors and to develop effective response strategies. This study takes a holistic, multi-disciplinary, system-focused approach to advance the fundamental knowledge of illicit drug use patterns in communities, which can help support effective multi-pronged responses from both the supply and demand sides. The project involves PIs from operations research, criminal justice, and public health policy, in collaboration with several regional agencies tasked with drug trafficking prevention. The project will engage and prepare graduate students to develop new analytical tools to respond to complex societal challenges.This project explores a novel EWS framework with transformative learning and optimization methodologies for identifying and responding to emerging illicit substance threats. The project will collect and build on the use of observational data from a variety of sources to build predictive and prescriptive models. In particular, this project will (1) develop a novel geospatially-aware predictive model to detect emerging threats of illicit drugs and identify high-risk communities by exploiting inherent geospatial connections in the data, (2) learn causal pathways through efficient algorithms to uncover the driving factors of the emerging threats among communities, (3) optimize dynamic intervention strategies that can adapt to emerging data from shifting epidemics, and (4) develop a decision support tool as a proof-of-concept of the proposed EWS framework. The predictive modeling and decision-analytic framework are generalizable to EWS in other application areas. The multidisciplinary team will partner with national and regional drug control programs to demonstrate the practical impact of the proposed data-driven EWS framework.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.