RAPID: Fine-Grained, Privacy-Responding Contact Traceback for COVID-19 Epidemiology

该项目名为CoV-2-Traceback，旨在通过促进疫情期间的接触者追踪过程，实现减轻COVID-19负面影响的方法。这种方法避免了明确的位置跟踪，而是使用手机上的精细信号监测技术来推断手机对的物理距离。这项工作还将尊重用户隐私，通过让用户控制系统收集的数据，通过三种方式：第一，系统收集的数据将存储在手机本身，第二，用户将有权从他们的手机中清除这些数据，或者选择完全退出系统，第三，每一步的追溯都将在个人用户同意的情况下进行。这种自动化和高度特异性的追溯将促进国民健康和保障国防，既加快了接触者追溯的进程，又将接触者追溯的效用扩展到大流行的后期阶段，其目标是延迟和降低每日感染率。从社会角度来看，这项工作旨在让蜂窝芯片组制造商、蜂窝网络提供商以及州和国家卫生当局参与到全国COVID-19缓解工作中来。CoV-2-Traceback通过自动识别和追溯确诊SARS-CoV-2病例的近期重大风险接触者，实现了减轻COVID-19负面影响的方法。GPS在室内和许多城市环境中都不能很好地工作，而信号处理算法不依赖GPS，而是检查蜂窝控制通道，以确定其他人是否接近确诊阳性病例，以及在多长时间内接近。目前的医学知识表明，最危险的接触涉及接触的时间和距离，但在以现有技术尚未满足的高度特异性识别此类接触方面存在挑战。该项目开发了一种回溯协议，可以解析新诊断的用户的手机标识符，然后将符合上述接近标准的手机标识提交给移动提供商，以便他们能够识别新诊断病例的密切接触者。随着COVID-19监测工作在每个州的加强，该项目将利用州和县的背景感染率来验证CoV-2-Traceback的准确性。与传统的接触者追踪方法相比，它还将量化该方法是否确实更具体，标记出最终被证明为COVID-19阴性的患者更少。该奖项反映了美国国家科学基金会的法定使命，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。

This project, CoV-2-Traceback, enables approaches that mitigate the negative effects of COVID-19 by facilitating the process of contact tracing during an epidemic. The approach eschews explicit location tracking, instead using granular signal monitoring techniques at mobile phones to infer the physical proximity of pairs of phones. The work will also respect user privacy, by giving users control over the data the system collects, in three ways: first, the data the system collects will be stored on the mobile phone itself, second, users will be empowered to clear that data from their phones, or opt-out of the system entirely, and third, each step of the traceback will occur with individual user consent. This automated and highly specific traceback will advance the national health and secure the national defense, both speeding up the process of contact traceback and extending the utility of contact traceback into the latter stages of a pandemic when the goal is to delay and lower daily infection rates. From a societal standpoint, the work aims to engage cellular chipset manufacturers, cellular network providers, and state and national health authorities in the national COVID-19 mitigation effort.CoV-2-Traceback enables approaches that mitigate the negative effects of COVID-19 by automating the identification and traceback of recent significant risk contacts of a confirmed SARS-CoV-2 case. Instead of relying on GPS, which does not work well indoors and in many urban settings, signal processing algorithms examine the cellular control channel to determine whether and for how long other people are proximal to a confirmed positive case. Current medical knowledge indicates that the riskiest exposures involve both time and proximity of contact, but there is a challenge in identifying such exposures with a high specificity that existing technology does not yet meet. The project develops a traceback protocol that resolves a newly-diagnosed user's phone identifiers and then submits phone identifies meeting the foregoing proximity criteria to cellular providers, so they can identify close contacts of the newly-diagnosed case. As COVID-19 surveillance efforts ramp up in each state, the project will leverage state- and county-level background infection rates to validate CoV-2-Traceback's accuracy. Comparing with traditional methods for contact tracing, it will also quantify whether the approach is indeed more specific, flagging fewer patients who in the end turn out to be COVID-19 negative.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.