论文标题
蓝牙低能测量的SARS-COV-2传输的风险估计
Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements
论文作者
论文摘要
基于蓝牙低能（BLE）的数字接触跟踪方法有可能有效地包含并延迟诸如持续的SARS-COV-2大流行等传染病暴发。在这项工作中，我们提出了一种新型基于机器学习的方法，以可靠地检测到足够的时间近距离的受试者，以便受到感染的风险。我们的研究是一个重要的概念证明，它将有助于流行病学政策，旨在减缓19009的迅速传播。
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.
