Contact Classification in COVID-19 Tracing

TL;DR

This paper analyzes methods for reliably classifying critical COVID-19 contacts using smartphone sensors to improve tracing accuracy and reduce unnecessary quarantines.

Contribution

It introduces a detailed analysis of contact classification techniques leveraging BLE and audio signals, considering different smartphone wearing positions.

Findings

Parameters identified for accurate contact warnings

Potential for significant disease spread reduction

Analysis of smartphone sensor capabilities and limitations

Abstract

The present paper addresses the task of reliably identifying critical contacts by using COVID-19 tracing apps. A reliable classification is crucial to ensure a high level of protection, and at the same time to prevent many people from being sent to quarantine by the app. Tracing apps are based on the capabilities of current smartphones to enable a broadest possible availability. Existing capabilities of smartphones include the exchange of Bluetooth Low Energy (BLE) signals and of audio signals, as well as the use of gyroscopes and magnetic sensors. The Bluetooth power measurements, which are often used today, may be complemented by audio ranging and attitude estimation in the future. Smartphones are worn in different ways, often in pockets and bags, which makes the propagation of signals and thus the classification rather unpredictable. Relying on the cooperation of users to wear their phones hanging from their neck would change the situation considerably. In this case the performance, achievable with BLE and audio measurements, becomes predictable. Our analysis identifies parameters that result in accurate warnings, at least within the scope of validity of the models. A significant reduction of the spreading of the disease can then be achieved by the apps, without causing many people to unduly go to quarantine. The present paper is the first of three papers which analyze the situation in some detail.