Author: Sattler, Felix; Ma, Jackie; Wagner, Patrick; Neumann, David; Wenzel, Markus; Schafer, Ralf; Samek, Wojciech; Muller, Klaus-Robert; Wiegand, Thomas
Title: Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements Cord-id: k5mzlgq9 Document date: 2020_4_22
ID: k5mzlgq9
Snippet: 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 spre
Document: 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.
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