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.

为了阻止埃博拉病毒的流行，必须进行有效的“接触跟踪”至关重要。问题很复杂，因为在诊断患者疾病后必须追溯进行接触追踪。接触追踪的任何失误可能都可能无法追踪处于危险和疾病传播的公民。临时追踪，依靠受感染的载体的回忆和人们相遇的人的回忆可能导致跟踪不准确。同样，依靠可能与承运人接触的个人的回忆无法准确识别那些处于高风险的人和患病风险低的人。该项目提出了一个框架，该框架使用现有的可用技术，该技术可以在不损害用户隐私的情况下进行有效的自动联系跟踪。该框架有两个部分：聚合器 - 从用户那里收集信息，以及分析仪 - - 处理要进行联系跟踪的信息。该项目将开发算法的进步，以最大程度地减少误报，上下文的存储功能和社交数据，这些功能既可扩展又可以保留潜在联系的隐私。当前管理埃博拉病毒流行的最佳方法是通过接触跟踪。也就是说，回顾性地确定与受影响的人联系的任何人。当前的接触跟踪系统是时间密集型且容易出错的。提出的系统将使用可扩展的隐私保存方法自动进行跟踪，该方法从受影响的人的社交网络和智能手机上下文中汲取数据。提出的框架不仅适用于接触埃博拉病毒的追踪，而且还可用于接触其他传染病的接触跟踪，以及在生物恐怖袭击的情况下进行的紧急准备。作为该项目的一部分开发的技术，例如保留存储架构的隐私，也可以应用于其他问题。