多层级数字接触者追踪
Multilevel Digital Contact Tracing
AI总结：
本文提出多层级数字接触者追踪框架，利用动态接触图、二元循环接触队列及感染路径算法识别直接与间接接触，并在合成和真实数据上验证其有效性。
摘要：
数字接触者追踪在缓解疫情暴发方面发挥着关键作用，而由于需要分析海量时序接触数据，为一个国家设计多层级数字接触者追踪仍是一个尚未解决的问题。我们开发了一个多层级数字接触者追踪框架，该框架根据近距离接触数据构建动态接触图。其中，我们将接触图的边标签引入为二元循环接触队列，用于保存潜伏期内的时序社会互动。随后，我们的算法从接触图中为给定的一组感染者准备直接和间接（多层级）接触者名单。最后，该算法为追踪名单构建感染路径。我们实现了该框架，并使用合成数据集和真实数据集验证了接触者追踪过程。此外，分析表明，对于 COVID-19 密切接触参数，该框架在生成感染路径时占用合理的空间和时间。通过更改算法参数，我们的框架可应用于任何流行病传播场景。
英文摘要：
Digital contact tracing plays a crucial role in alleviating an outbreak, and designing multilevel digital contact tracing for a country is an open problem due to the analysis of large volumes of temporal contact data. We develop a multilevel digital contact tracing framework that constructs dynamic contact graphs from the proximity contact data. Prominently, we introduce the edge label of the contact graph as a binary circular contact queue, which holds the temporal social interactions during the incubation period. After that, our algorithm prepares the direct and indirect (multilevel) contact list for a given set of infected persons from the contact graph. Finally, the algorithm constructs the infection pathways for the trace list. We implement the framework and validate the contact tracing process with synthetic and real-world data sets. In addition, analysis reveals that for COVID-19 close contact parameters, the framework takes reasonable space and time to create the infection pathways. Our framework can apply to any epidemic spreading by changing the algorithm's parameters.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
