Identifying "superspreaders" of disease is a pressing concern for society during pandemics such as COVID-19. Superspreaders represent a group of people who have much more social contacts than others. The widespread deployment of WLAN infrastructure enables non-invasive contact tracing via people's ubiquitous mobile devices. This technology offers promise for detecting superspreaders. In this paper, we propose a general framework for WLAN-log-based superspreader detection. In our framework, we first use WLAN logs to construct contact graphs by jointly considering human symmetric and asymmetric interactions. Next, we adopt three vertex centrality measurements over the contact graphs to generate three groups of superspreader candidates. Finally, we leverage SEIR simulation to determine groups of superspreaders among these candidates, who are the most critical individuals for the spread of disease based on the simulation results. We have implemented our framework and evaluate it over a WLAN dataset with 41 million log entries from a large-scale university. Our evaluation shows superspreaders exist on university campuses. They change over the first few weeks of a semester, but stabilize throughout the rest of the term. The data also demonstrate that both symmetric and asymmetric contact tracing can discover superspreaders, but the latter performs better with daily contact graphs. Further, the evaluation shows no consistent differences among three vertex centrality measures for long-term (i.e., weekly) contact graphs, which necessitates the inclusion of SEIR simulation in our framework. We believe our proposed framework and these results may provide timely guidance for public health administrators regarding effective testing, intervention, and vaccination policies.

中文翻译：

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COVID-19大流行中基于WLAN日志的超级扩展器检测

在大流行期间（例如COVID-19），识别疾病的“超级传播者”是社会关注的紧迫问题。超级传播者代表了一群比其他人具有更多社交联系的人。WLAN基础设施的广泛部署使人们可以通过无处不在的移动设备进行无创的联系人跟踪。这项技术为检测超级吊具提供了希望。在本文中，我们提出了基于WLAN日志的超级扩展器检测的通用框架。在我们的框架中，我们首先使用WLAN日志通过共同考虑人的对称和非对称交互来构建联系图。接下来，我们对接触图采用三个顶点中心度测量，以生成三组超级扩展候选。最后，我们利用SEIR模拟来确定这些候选者中的超级扩展器组，根据模拟结果，他们是疾病传播的最关键人物。我们已经实施了我们的框架，并通过具有来自大型大学的4,100万条日志条目的WLAN数据集对其进行了评估。我们的评估表明，超级传播者存在于大学校园中。在一个学期的前几周，他们会有所变化，但在整个学期的其余时间内都保持稳定。数据还表明，对称和非对称接触跟踪都可以发现超级传播者，但超级传播者在日常联系图中的表现更好。此外，对于长期（即每周）接触图，评估显示三种顶点中心度度量之间没有一致的差异，因此有必要在我们的框架中包括SEIR仿真。