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

为了遏制埃博拉疫情，进行有效的“接触者追踪”至关重要。这个问题很复杂，因为接触者追踪必须在患者被诊断出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到面临疾病风险和传播的公民。临时追踪，依靠被感染的携带者？S回忆去过的地方和见过的人，可能会导致追踪不准确。同样，依靠可能接触过携带者的个人的回忆，不能准确地识别高危人群和低风险人群。该项目提出了一个框架，该框架使用现有的现成技术，可以在不损害用户隐私的情况下进行有效的、自动的接触者追踪。该框架由两个部分组成：聚合器--从用户那里收集信息，以及分析器-处理信息以进行联系人跟踪。该项目将开发算法方面的进步，以最大限度地减少误报，存储上下文和社交数据的功能，既可扩展，又将保护潜在联系人的隐私。目前控制埃博拉疫情的最佳方法是通过接触者追踪；也就是说，追溯识别任何与感染者有过接触的人。目前的接触者追踪系统时间密集且容易出错。建议的系统将使用一种可扩展的隐私保护方法自动进行跟踪，该方法从受影响的人-S社交网络和智能手机上下文中提取数据。该框架不仅适用于埃博拉病毒的接触者追踪，还可用于其他传染病的接触者追踪，以及在生物恐怖袭击情况下的应急准备。作为该项目的一部分而开发的技术，如隐私保护存储体系结构，也可以应用于其他问题。

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.