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Digital contact tracing approaches based on Bluetooth low energy (BLE) havethe potential to efficiently contain and delay outbreaks of infectious diseasessuch as the ongoing SARS-CoV-2 pandemic. In this work we propose a novelmachine learning based approach to reliably detect subjects that have spentenough time in close proximity to be at risk of being infected. Our study is animportant proof of concept that will aid the battery of epidemiologicalpolicies aiming to slow down the rapid spread of COVID-19.

Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements