基于蓝牙低功耗测量的新型冠状病毒传播风险评估
Risk estimation of SARS-CoV-2 transmission from bluetooth low energy measurements.
作者信息
Sattler Felix, Ma Jackie, Wagner Patrick, Neumann David, Wenzel Markus, Schäfer Ralf, Samek Wojciech, Müller Klaus-Robert, Wiegand Thomas
机构信息
Fraunhofer Heinrich Hertz Institute, 10587 Berlin, Germany.
Department of Electrical Engineering and Computer Science, Technische Universität Berlin, 10587 Berlin, Germany.
出版信息
NPJ Digit Med. 2020 Oct 6;3:129. doi: 10.1038/s41746-020-00340-0. eCollection 2020.
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.
基于低功耗蓝牙（BLE）的数字接触者追踪方法有潜力有效控制和延缓传染病的爆发，如当前的新冠病毒大流行。在这项工作中，我们提出了一种基于机器学习的方法，以可靠地检测出那些与他人近距离接触足够长时间、有被感染风险的个体。我们的研究是一个重要的概念验证，将有助于一系列旨在减缓新冠病毒快速传播的流行病学政策。
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