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基于测量的谷歌/苹果接触者追踪 API 在轻轨电车上接近检测性能评估。

Measurement-based evaluation of Google/Apple Exposure Notification API for proximity detection in a light-rail tram.

机构信息

School of Computer Science & Statistics, Trinity College Dublin, Dublin, Ireland.

出版信息

PLoS One. 2020 Sep 30;15(9):e0239943. doi: 10.1371/journal.pone.0239943. eCollection 2020.

Abstract

We report on the results of a Covid-19 contact tracing app measurement study carried out on a standard design of European commuter tram. Our measurements indicate that in the tram there is little correlation between Bluetooth received signal strength and distance between handsets. We applied the detection rules used by the Italian, Swiss and German apps to our measurement data and also characterised the impact on performance of changes in the parameters used in these detection rules. We find that the Swiss and German detection rules trigger no exposure notifications on our data, while the Italian detection rule generates a true positive rate of 50% and a false positive rate of 50%. Our analysis indicates that the performance of such detection rules is similar to that of triggering notifications by randomly selecting from the participants in our experiments, regardless of proximity.

摘要

我们报告了在标准设计的欧洲通勤电车上进行的 Covid-19 接触者追踪应用测量研究的结果。我们的测量结果表明,在电车内,蓝牙接收信号强度与手机之间的距离几乎没有相关性。我们将意大利、瑞士和德国应用程序使用的检测规则应用于我们的测量数据,并研究了这些检测规则中参数变化对性能的影响。我们发现,瑞士和德国的检测规则在我们的数据上不会触发暴露通知,而意大利的检测规则的真阳性率为 50%,假阳性率为 50%。我们的分析表明,无论距离如何,这些检测规则的性能与从实验参与者中随机选择来触发通知的性能相似。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d345/7526892/aa58519bf8fc/pone.0239943.g001.jpg

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