Continuous Learning and Inference of Individual Probability of SARS-CoV-2 Infection Based on Interaction Data
This work addresses the challenge of efficiently detecting asymptomatic COVID-19 carriers for public health management, representing a domain-specific incremental improvement.
This study tackled the problem of identifying asymptomatic carriers of SARS-CoV-2 by developing a continuous learning and inference approach based on interaction data, which reduced screening and quarantine requirements by up to 94% compared to traditional contact tracing methods.
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%.