Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements
This work addresses the need for efficient digital contact tracing to contain infectious disease outbreaks like COVID-19, though it appears incremental as it builds on existing BLE-based methods.
The authors tackled the problem of estimating SARS-CoV-2 transmission risk using Bluetooth Low Energy (BLE) measurements by proposing a novel machine learning approach to detect close-proximity contacts at risk of infection, demonstrating a proof of concept to aid in slowing the spread of COVID-19.
Digital contact tracing approaches based on Bluetooth low energy (BLE) have the potential to efficiently contain and delay outbreaks of infectious diseases such as the ongoing SARS-CoV-2 pandemic. In this work we propose a novel machine learning based approach to reliably detect subjects that have spent enough time in close proximity to be at risk of being infected. Our study is an important proof of concept that will aid the battery of epidemiological policies aiming to slow down the rapid spread of COVID-19.