  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.
