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.

中文翻译：

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通过蓝牙低功耗测量评估 SARS-CoV-2 传播的风险

基于蓝牙低功耗 (BLE) 的数字接触者追踪方法有可能有效遏制和延迟传染病的爆发，例如正在进行的 SARS-CoV-2 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，可以可靠地检测那些花费足够时间近距离接触有被感染风险的受试者。我们的研究是一个重要的概念验证，将有助于制定一系列旨在减缓 COVID-19 快速传播的流行病学政策。