Finding Asymptomatic Spreaders in a COVID-19 Transmission Network by Graph Attention Networks

Abstract

In the COVID-19 epidemic the mildly symptomatic and asymptomatic infections generate a substantial portion of virus spread; these undetected individuals make it difficult to assess the effectiveness of preventive measures as most epidemic prevention strategies are based on the detected data. Effectively identifying the undetected infections in local transmission will be of great help in COVID-19 control. In this work, we propose an RNA virus transmission network representation model based on graph attention networks (RVTR); this model is constructed using the principle of natural language processing to learn the information of gene sequence and using a graph attention network to catch the topological character of COVID-19 transmission networks. Since SARS-CoV-2 will mutate when it spreads, our approach makes use of graph context loss function, which can reflect that the genetic sequence of infections with close spreading relation will be more similar than those with a long distance, to train our model. Our approach shows its ability to find asymptomatic spreaders both on simulated and real COVID-19 datasets and performs better when compared with other network representation and feature extraction methods.

Editorial: Infectious Disease Epidemiology and Transmission Dynamics

“…Our research mainly focuses on COVID-19, an urgent problem to solve in 2022 with the emergence of multiple SARS-CoV-2 variants that can escape human immunity elicited by previous infection or vaccination [ 16 , 17 ]. Du et al and Jin et al reviewed and estimated the reproduction number of SARS-CoV-2 variants (e.g., Omicron, Delta) to evaluate their transmission advantages [ 16 , 18 ] and found that asymptomatic spreaders could be identified from the transmission network [ 19 ]. Mass vaccination, treatment, and mass testing can help reduce the overall population-level attack rate and prove critical to early pandemic mitigation.…”