D-ISN: TRACK 2: Collaborative Research: Financial Network Disruptions in Illicit and Counterfeit Medicines (FIND-M)

D-ISN：轨道 2：合作研究：非法和假冒药品的金融网络中断 (FIND-M)

• 批准号: 2039945
• 负责人: Nikos Passas
• 金额: $ 17万
• 依托单位: Northeastern University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2039945&HistoricalAwards=false
• 关键词: ISN; TRACK; Collaborative; Research; Financial

Counterfeit and illegal drugs cause mortality and morbidity for millions of people around the world, as well as damage brands, undermine competition and the rule of law, cause economic losses and security threats, and corrupt financial systems. In light of the global coronavirus pandemic, there is an urgent need to develop a multipronged approach, including access to critical data, network analysis, distributed inference, identification of strategic points of intervention, and mitigation measures to disrupt the flow of counterfeit and illegal medicines in both high and low income countries. Identifying chokepoints (similar to other distribution networks) to effectively disrupt illegal medical supply chains is going to be an important feature of the project. If a solution to this challenge is not found, then prevention and enforcement successes will be partial, illegal entrepreneurs will adapt their modus operandi to circumvent controls, and public health, revenue, fair competition, justice, and security concerns will remain largely unaddressed. This Disrupting Operations of Illicit Supply Networks (D-ISN) planning has the potential to refine questions and solutions that can transform the national, state, and community-level discussions around illegal and counterfeit medicines. This collective effort will introduce a new governance and social control model whereby government, private sector, and academic parties are motivated to share skills, knowledge, and data to tackle the important social problems instigated by illicit entrepreneurs and criminal networks. The goal of this planning grant - bringing together stakeholders from academic, law-enforcement, public and private sectors - is to develop a distributed data infrastructure, populate this infrastructure, and conduct exploratory research in order to leverage financial, commercial and business data, along with previous best practices (from human trafficking and trade-based money laundering controls) for effective disruption of illegal medical and pharmaceutical supply chains. We aim to create robust approaches that will prevent or minimize the social harm caused by these illicit networks and we will coordinate novel, cutting edge efforts to improve outcomes for those victimized. Our specific objectives are to: 1) assemble the stakeholders and partners from other research communities to identify criminogenic asymmetries in the illicit supply networks of counterfeit and illegal drugs; 2) develop a task force, build out the infrastructure, and a detailed plan on how to mine distributed data (financial, business, commercial) using explainable machine learning methods to infer information needed to generate the multiplex networks; 3) stand up a task force, build multiplex networks that capture links discovered by mining the financial, business and commercial data, and develop a detailed research plan on how to discover the “weak-links”; 4) develop a task force, design mitigation strategies, and perform exploratory research on testing the products indicated by our analysis. By design, the project's hypotheses are broad at this stage, in order to incorporate inputs from the diverse stakeholders and partners, and to narrow down the focus during the planning stages of the project. Partners include representatives from trade, public health, and anti-counterfeiting teams both national and international. This research will be informed by the latest work in the area and specific scholars will be asked to join the academic team. Initially, the team will work with historical data, but there is a plan to work with several large financial institutions to run the algorithms we develop in a distributed, secure and privacy-preserving manner on current and live sources.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.

伪造和非法毒品会给全球数百万人带来死亡和发病率，以及损害品牌，破坏竞争和法治，造成经济损失和安全威胁以及腐败的金融系统。鉴于全球冠状病毒大流行，迫切需要开发一种多管应的方法，包括获取关键数据，网络分析，分布式推理，战略性的识别以及减轻措施，以破坏高收入和低收入国家的假冒和非法药物的流动。识别扼流口（类似于其他分销网络）以有效破坏非法医疗供应链将是该项目的重要特征。如果找不到解决这一挑战的解决方案，那么预防和执法成功将是部分的，非法企业家将适应其作案手法以规避控制，公共卫生，收入，公平竞争，正义，正义和安全问题将在很大程度上保持不足。非法供应网络（D-ISN）计划的这种破坏性运营有可能完善问题和解决方案，这些问题和解决方案可以改变围绕非法和伪造药物的国家，州和社区级别的讨论。这项集体努力将引入一种新的治理和社会控制模式，政府，私营部门和学术方有动机共享技能，知识和数据，以解决非法企业家和犯罪网络所激发的重要社会问题。这项计划赠款的目标 - 将学术，法律措施，公共和私营部门的利益相关者汇集在一起​​ - 是为了开发分布式数据基础架构，填充该基础架构并进行探索性研究，以便利用金融，商业和商业数据，以及以前的最佳实践以及基于人口运输和基于贸易的货币供应式供应，并有效地供应了秘密的医疗服务。我们旨在创造强大的方法，以防止或最大程度地减少这些非法网络造成的社会伤害，我们将协调新颖的，尖端的努力，以改善被虐待者的成果。我们的具体目标是：1）在其他研究社区的利益相关者和合作伙伴组装中，以鉴定伪造和非法药物的非法供应网络中的犯罪不对称； 2）使用可解释的机器学习方法来挖掘分布式数据（财务，商业，商业）的详细计划，并制定一个工作组，建立基础架构，并使用可解释的机器学习方法来推断生成多重网络所需的信息； 3）站立一个工作组，建立多重网络，通过挖掘财务，商业和商业数据来捕获发现的联系，并制定有关如何发现“弱链接”的详细研究计划； 4）制定工作队，设计缓解策略，并对我们的分析指示的产品进行探索性研究。根据设计，该项目的假设在此阶段很广泛，以结合潜水员利益相关者和合作伙伴的意见，并在项目的计划阶段缩小焦点。合作伙伴包括代表贸易，公共卫生和国际和国际的反企业团队。该研究将由该地区的最新工作告知，并要求特定的学者加入学术团队。最初，该团队将与历史数据合作，但是有一个计划与几家大型金融机构合作，以分布式，安全和隐私的方式来运行我们开发的算法，以当前和现场源头来源。该奖项反映了NSF的法定任务，并通过评估该基金会的知识点功能和广泛影响来评估NSF的法定任务。