D-ISN/Collaborative Research: Disrupting West Virginia's Opioid Crisis: a Multi-disciplinary Approach through Interdiction and Harm Reduction
D-ISN/合作研究:扰乱西弗吉尼亚州的阿片类药物危机:通过拦截和减少危害采取多学科方法
基本信息
- 批准号:2240361
- 负责人:
- 金额:$ 23.87万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2023
- 资助国家:美国
- 起止时间:2023-09-01 至 2026-08-31
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
This Disrupting Operations of Illicit Supply Networks (D-ISN) project develops novel analytical methods to assess the effectiveness of interventions to disrupt opioid supply chains. At the local level, options to disrupt these networks can be broadly categorized into two groups: (i) supply-side interdiction strategies, which focus on disrupting the drug flow into the communities, and (ii) demand-side interdiction strategies, which concentrate on reducing drug demand and on mitigating the health impacts of drug use within communities. Supply- and demand-side interdiction approaches are often seen as exclusive and competing approaches for handling the opioid epidemic. They are, however, synergistic as supply-side interdiction reduces the preponderance of drugs in communities whereas harm-reduction mitigates their adverse effects. Finding balance between these interventions is particularly crucial for counties as they house many important governmental functions in emergency, social, and public safety services. Driven by empirical investigations of opioid network features at the local level, this project aims to develop decision support models that are powered by new advances in machine learning and utility theory to help county policy makers allocate their resources effectively to combat this epidemic. The models will be validated using data collected from the state of West Virginia, which has been uniquely negatively affected by opioids. The project will provide support for graduate students, who will be trained in multidisciplinary approaches to address complex societal problems.This project will link county expenditure data with available public health and public safety data on drug availability and use. In particular, the project (i) maps and compares West Virginia counties in terms of budgetary policy directions (supply-side, demand-side or both) and their relative success at disrupting the supply and demand for drugs in local communities and at reducing their negative health consequences and (ii) builds analytical models that tackle aspects of opioid network disruption at multiple state decision levels. These models will provide data-driven prescriptions for budget allocation taking into account three intrinsic challenges: (a) the forms of utilities of policymakers are not known exactly, (b) the health and safety consequences of budget decisions are complex nonlinear functions accessible through data, and (c) opioid networks adapt to policy decisions. Developed models and solution methodologies will build on recent developments in utility theory, machine learning, and optimization to provide local counties decision tools for policy prescriptions that can lead to optimal/improved outcomes.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.
该项目开发了新的分析方法,以评估干预措施破坏阿片类药物供应链的有效性。 在地方一级,破坏这些网络的备选办法大致可分为两类:供应方阻截战略,侧重于阻断毒品流入社区;需求方阻截战略,侧重于减少毒品需求和减轻社区内吸毒对健康的影响。供应方和需求方阻截办法往往被视为处理类阿片流行病的排他性和相互竞争的办法。然而,它们是协同增效的,因为供应方的阻截减少了社区中毒品的优势,而减少危害则减轻了其不利影响。在这些干预措施之间找到平衡对县来说尤其重要,因为它们在紧急情况,社会和公共安全服务方面拥有许多重要的政府职能。在地方一级阿片类药物网络特征的实证调查的驱动下,该项目旨在开发决策支持模型,这些模型由机器学习和效用理论的新进展提供支持,以帮助县政策制定者有效地分配资源,以对抗这种流行病。这些模型将使用从西弗吉尼亚州收集的数据进行验证,西弗吉尼亚州受到阿片类药物的独特负面影响。该项目将为研究生提供支助,对他们进行多学科方法培训,以解决复杂的社会问题,该项目将把州支出数据与关于药物供应和使用的现有公共卫生和公共安全数据联系起来。特别是,该项目(一)绘制和比较西弗吉尼亚州各县的预算政策方向(供应方,需求方或两者)及其在扰乱当地社区药物供需和减少其负面健康后果方面的相对成功,(二)建立分析模型,在多个州决策层面解决阿片类药物网络中断的问题。这些模型将为预算分配提供数据驱动的处方,同时考虑到三个内在挑战:(a)决策者的效用形式并不确切,(B)预算决定的健康和安全后果是可通过数据获得的复杂非线性函数,(c)类阿片网络适应政策决定。开发的模型和解决方案方法将建立在效用理论、机器学习和优化的最新发展基础上,为当地县提供政策处方决策工具,从而实现最佳/改善的结果。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。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Jean-Philippe Richard其他文献
Jean-Philippe Richard的其他文献
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{{ truncateString('Jean-Philippe Richard', 18)}}的其他基金
Collaborative Research: Novel Relaxations for Cardinality-constrained Optimization Problems with Applications in Network Interdiction and Data Analysis
协作研究:基数约束优化问题的新颖松弛及其在网络拦截和数据分析中的应用
- 批准号:
1917323 - 财政年份:2018
- 资助金额:
$ 23.87万 - 项目类别:
Standard Grant
Collaborative Research: Novel Relaxations for Cardinality-constrained Optimization Problems with Applications in Network Interdiction and Data Analysis
协作研究:基数约束优化问题的新颖松弛及其在网络拦截和数据分析中的应用
- 批准号:
1728031 - 财政年份:2017
- 资助金额:
$ 23.87万 - 项目类别:
Standard Grant
Collaborative Research: Novel Tighter Relaxations for Complementarity Constraints with Applications to Nonlinear and Bilevel Programming
协作研究:互补约束的新颖更严格松弛及其在非线性和双层规划中的应用
- 批准号:
1235236 - 财政年份:2012
- 资助金额:
$ 23.87万 - 项目类别:
Standard Grant
New Modeling and Solution Paradigms for Transportation Problems with Applications to Railroads
运输问题的新建模和解决方案及其在铁路中的应用
- 批准号:
1200616 - 财政年份:2012
- 资助金额:
$ 23.87万 - 项目类别:
Standard Grant
Collaborative Research: Generating Stronger Cuts for Nonlinear Programs Via Orthogonal Disjunctions and Lifting Techniques
协作研究:通过正交析取和提升技术为非线性程序生成更强的削减
- 批准号:
0856605 - 财政年份:2009
- 资助金额:
$ 23.87万 - 项目类别:
Standard Grant
CAREER: Improving the Optimization and Re-Optimization of Mixed Integer Programs through the Study of Continuous Variables
职业:通过连续变量的研究改进混合整数程序的优化和重新优化
- 批准号:
0958824 - 财政年份:2009
- 资助金额:
$ 23.87万 - 项目类别:
Continuing Grant
CAREER: Improving the Optimization and Re-Optimization of Mixed Integer Programs through the Study of Continuous Variables
职业:通过连续变量的研究改进混合整数程序的优化和重新优化
- 批准号:
0348611 - 财政年份:2004
- 资助金额:
$ 23.87万 - 项目类别:
Continuing Grant
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