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Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements
- Authors
- Sattler, Felix; Ma, Jackie; Wagner, Patrick; Neumann, David; Wenzel, Markus; Schaefer, Ralf; Samek, Wojciech; Mueller, Klaus-Robert; Wiegand, Thomas
- Issue Date
- 6-10월-2020
- Publisher
- NATURE RESEARCH
- Citation
- NPJ DIGITAL MEDICINE, v.3, no.1
- Indexed
- SCIE
SCOPUS
- Journal Title
- NPJ DIGITAL MEDICINE
- Volume
- 3
- Number
- 1
- ISSN
- 2398-6352
- Abstract
- 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 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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Collections - Graduate School > Department of Artificial Intelligence > 1. Journal Articles
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