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

点击翻译按钮获取中文摘要

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.