Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy\n Measurements Article Swipe

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Bluetooth Bluetooth Low Energy Coronavirus disease 2019 (COVID-19) Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Contact tracing Pandemic Computer science Transmission (telecommunications) 2019-20 coronavirus outbreak Energy (signal processing) Tracing Computer security Real-time computing Outbreak Telecommunications Virology Wireless Infectious disease (medical specialty) Medicine Statistics Mathematics Operating system Disease Pathology

Felix Sattler , Jackie Ma , Patrick Wagner , David Neumann , M Wenzel , Ralf B. Schäfer , Wojciech Samek , Klaus‐Robert Müller , Thomas Wiegand ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2004.11841 · OA: W4287812855

Digital contact tracing approaches based on Bluetooth low energy (BLE) have\nthe potential to efficiently contain and delay outbreaks of infectious diseases\nsuch as the ongoing SARS-CoV-2 pandemic. In this work we propose a novel\nmachine learning based approach to reliably detect subjects that have spent\nenough time in close proximity to be at risk of being infected. Our study is an\nimportant proof of concept that will aid the battery of epidemiological\npolicies aiming to slow down the rapid spread of COVID-19.\n