RAPID: SCH: A Framework for Epidemic Contact Tracing Using Multi-contextual Information

RAPID：SCH：使用多上下文信息进行流行病接触者追踪的框架

• 批准号: 1513369
• 负责人: Krishna Kavi
• 金额: $ 10万
• 依托单位: University of North Texas
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-12-01 至 2015-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1513369&HistoricalAwards=false
• 关键词: RAPID; SCH; Framework; Epidemic; Contact

In order to stop the Ebola Epidemic it is essential to do effective "contact tracing." The problem is complex as the contact tracing has to be done retroactively after a patient is diagnosed with the disease. Any lapse in contact tracing could potentially fail to track citizens at risk and spreading of the disease. Ad hoc tracing, relying on the infected carrier?s recollection of places visited and people met may lead to inaccurate tracking. Similarly, relying on the recollections of individuals who may have come into contact with the carrier cannot accurately identify those who are at high risk and those who are at low risk of contracting the disease. This project proposes a framework that uses existing readily available technologies that can do effective, automated contact tracing without compromising the privacy of the users. The framework has two parts: aggregator - which collects information from the users, and analyzer --- which processes the information to do contact tracing. This project will develop advances in algorithms to minimize false positives, storage features for context and social data that is both scalable and will preserve privacy of potential contacts. The best current method for managing the Ebola epidemic currently is through contact tracing; that is, retrospectively identifying anyone with whom the affected person has come in contact. The current contact tracing system is time intensive and error-prone. The proposed system will do the tracing automatically, using a scalable, privacy preserving method that draws data from the affected person?s social network and smart phone context. The proposed framework is not only applicable to contact tracing for Ebola, but also can be used for contact tracing of other infectious diseases, as well as emergency preparedness in the case of bio-terrorism attacks. The technologies developed as part of this project, such as privacy preserving storage architecture, can also be applied to other problems.

为了阻止埃博拉疫情，必须进行有效的“接触者追踪”。“这个问题很复杂，因为接触者追踪必须在病人被诊断患有这种疾病后追溯进行。接触者追踪方面的任何失误都可能无法追踪处于疾病传播风险中的公民。特别追踪，依靠被感染的携带者？的回忆访问的地方和人见面可能会导致不准确的跟踪。同样，依靠可能与携带者接触过的个人的回忆，也不能准确地确定哪些人感染这种疾病的风险高，哪些人感染这种疾病的风险低。该项目提出了一个框架，使用现有的现成的技术，可以做有效的，自动化的联系人跟踪，而不损害用户的隐私。该框架有两个部分：聚合器-从用户那里收集信息，分析器-处理信息以进行联系人跟踪。该项目将开发算法的进步，以最大限度地减少误报，上下文和社交数据的存储功能，这些功能既可扩展，又可保护潜在联系人的隐私。目前管理埃博拉疫情的最佳方法是通过接触者追踪，即追溯性地识别与受影响者接触过的任何人。目前的接触者追踪系统耗时且容易出错。拟议中的系统将自动跟踪，使用可扩展的，隐私保护的方法，从受影响的人？的社交网络和智能手机上下文。拟议框架不仅适用于埃博拉接触者追踪，也可用于其他传染病的接触者追踪，以及生物恐怖主义袭击情况下的应急准备。作为该项目的一部分开发的技术，如隐私保护存储架构，也可以应用于其他问题。