IEEE Spectrum February 2, 2021
An international team of researchers (Germany, USA – industry) has developed a machine learning algorithm 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. It is based on multi-layer bidirectional path tracking and inference searching. The individual directed graph is determined by the appearance timeline and spatial data that can adapt over time, taking into account the incubation period and several features that can represent real-world circumstances, such as the number of asymptomatic carriers present. The model collects the interaction features and infers the individual person’s probability of getting infected using the status of the surrounding people. In tests, compared to traditional contact tracing methods, CLIP significantly reduces the screening and quarantine required to search for the potential asymptomatic virus carriers by as much as 94%…read more. Open Access TECHNICAL ARTICLEÂ
The learning and inference scheme of the CLIIP model. Credit: Scientific Reports volume 11, Article number: 2624 (2021)