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

RAPID：针对 COVID-19 流行病学的细粒度、隐私响应型接触者追溯

基本信息

批准号：
2027647
负责人：
Kyle Jamieson
金额：
$ 10万
依托单位：
Princeton 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：
2020-05-01 至 2022-04-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027647&HistoricalAwards=false
关键词：
RAPID Fine Grained Privacy Responding

项目摘要

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.

这个名为CoV-2-Traceback的项目通过促进疫情期间的接触者追踪过程，实现了减轻COVID-19负面影响的方法。该方法避免了显式的位置跟踪，而是在移动的电话上使用粒度信号监测技术来推断成对电话的物理接近度。这项工作还将尊重用户隐私，通过三种方式让用户控制系统收集的数据：首先，系统收集的数据将存储在移动的手机上，其次，用户将有权从手机中清除数据，或者选择完全退出系统，第三，追溯的每一步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家健康并确保国防安全，既加快了接触追溯的过程，又将接触追溯的实用性扩展到大流行的后期阶段，目标是延迟和降低每日感染率。从社会的角度来看，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国家COVID-19缓解工作。CoV-2-Traceback通过自动识别和追溯确诊的SARS-CoV-2病例的近期重大风险接触者，实现减轻COVID-19负面影响的方法。信号处理算法不再依赖GPS，因为GPS在室内和许多城市环境中工作效果不佳，而是检查蜂窝控制信道，以确定其他人是否接近确诊阳性病例以及距离该病例有多长时间。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，但在以现有技术尚不能满足的高度特异性识别这种接触方面存在挑战。该项目开发了一种追溯协议，该协议解析新诊断用户的电话标识符，然后将符合上述接近标准的电话标识符提交给蜂窝提供商，以便他们能够识别新诊断病例的密切接触者。随着各州COVID-19监测工作的加强，该项目将利用州和县一级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，标记最终证明为COVID-19阴性的患者更少。该奖项反映了NSF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。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Invited Paper: The Case for Small-Scale, Mobile-Enhanced COVID-19 Epidemiology

特邀论文：小规模、移动增强的 COVID-19 流行病学案例

DOI：
10.23919/wiopt52861.2021.9589290
发表时间：
2021
期刊：
and Wireless Networks (WiOpt
影响因子：
0
作者：
Yi, Fan;Xie, Yaxiong;Jamieson, Kyle
通讯作者：
Jamieson, Kyle

Cellular-Assisted, Deep Learning Based COVID-19 Contact Tracing

DOI：
10.1145/3550332
发表时间：
2022-09
期刊：
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
影响因子：
0
作者：
Fan Yi;Yaxiong Xie;Kyle Jamieson
通讯作者：
Fan Yi;Yaxiong Xie;Kyle Jamieson

Cellular-Assisted COVID-19 Contact Tracing

蜂窝辅助 COVID-19 接触者追踪