  This study presents a new approach to determine the likelihood of
asymptomatic carriers of the SARS-CoV-2 virus by using interaction-based
continuous learning and inference of individual probability (CLIIP) for
contagious ranking. This approach is developed based on an individual directed
graph (IDG), using multi-layer bidirectional path tracking and inference
searching. The IDG is determined by the appearance timeline and spatial data
that can adapt over time. Additionally, the approach takes into consideration
the incubation period and several features that can represent real-world
circumstances, such as the number of asymptomatic carriers present. After each
update of confirmed cases, the model collects the interaction features and
infers the individual person's probability of getting infected using the status
of the surrounding people. The CLIIP approach is validated using the
individualized bidirectional SEIR model to simulate the contagion process.
Compared to traditional contact tracing methods, our approach significantly
reduces the screening and quarantine required to search for the potential
asymptomatic virus carriers by as much as 94%.
