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.

翻译：数字接触追踪在缓解疫情爆发中起着关键作用，然而由于需要分析大量时序接触数据，为国家设计多层数字接触追踪仍是一个开放性问题。我们提出了一种多层数字接触追踪框架，该框架通过近距接触数据构建动态接触图。值得注意的是，我们引入了接触图的边标签作为二进制环形接触队列，该队列持有潜伏期内的时序社交交互信息。随后，我们的算法从接触图中为给定感染人群生成直接和间接（多层）接触列表。最后，算法为追踪列表构建感染路径。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追踪过程。此外，分析表明，对于COVID-19密切接触参数，该框架在构建感染路径时占用合理的空间与时间。通过调整算法参数，该框架可适用于任意流行病传播场景。