  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.
