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

arXiv: https://arxiv.org/abs/2004.11841 · HTML: https://mlsanity.com/paper/2004.11841/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [Risk score learning for COVID-19 contact tracing apps](https://mlsanity.com/paper/2104.08415/) (2021)

## Abstract (arXiv)

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.