Continuous Learning and Inference of Individual Probability of SARS-CoV-2 Infection Based on Interaction Data

TL;DR

This paper introduces CLIIP, a novel continuous learning method using interaction data and graph modeling to accurately infer individual SARS-CoV-2 infection probabilities, significantly improving contact tracing efficiency.

Contribution

The paper presents a new interaction-based continuous learning approach with an adaptive graph model for real-time infection probability inference.

Findings

Reduces screening and quarantine efforts by up to 94%

Validates approach with simulated contagion data

Enhances contact tracing accuracy and efficiency

Abstract

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%.