A New Way to Trace SARS-CoV-2 Variants Through Weighted Network Analysis of Frequency Trajectories of Mutations

Abstract

Early detection of SARS-CoV-2 variants enables timely tracking of clinically important strains in order to inform the public health response. Current subtype-based variant surveillance depending on prior subtype assignment according to lag features and their continuous risk assessment may delay this process. We proposed a weighted network framework to model the frequency trajectories of mutations (FTMs) for SARS-CoV-2 variant tracing, without requiring prior subtype assignment. This framework modularizes the FTMs and conglomerates synchronous FTMs together to represent the variants. It also generates module clusters to unveil the epidemic stages and their contemporaneous variants. Eventually, the module-based variants are assessed by phylogenetic tree through sub-sampling to facilitate communication and control of the epidemic. This process was benchmarked using worldwide GISAID data, which not only demonstrated all the methodology features but also showed the module-based variant identification had highly specific and sensitive mapping with the global phylogenetic tree. When applying this process to regional data like India and South Africa for SARS-CoV-2 variant surveillance, the approach clearly elucidated the national dispersal history of the viral variants and their co-circulation pattern, and provided much earlier warning of Beta (B.1.351), Delta (B.1.617.2), and Omicron (B.1.1.529). In summary, our work showed that the weighted network modeling of FTMs enables us to rapidly and easily track down SARS-CoV-2 variants overcoming prior viral subtyping with lag features, accelerating the understanding and surveillance of COVID-19.

Early detection of SARS-CoV-2 variants through dynamic co-mutation network surveillance

“…This approach identifies emerging communities and their branching relationships with the latest circulating ones, indicating novel variants and their evolutionary relationships. This strategy contrasts most of previous researches that focuses on discovery of individual communities ( 9 , 16 ).…”

“…Most of these studies focus on trend survey of viral mutations ( 3 , 17 , 18 ) or their phenetic clustering ( 11 , 19 ) but not real variant monitoring. Time-series clustering of frequency trajectories of mutations has been found to be an efficient tool in variant discovery and prediction ( 9 , 16 ). Challenges arise in interpreting these results due to discrepancies in cluster features of the same variants that hinder comparisons of horizontal (between-country) or longitudinal (across-time) monitoring results.…”

“…Due to sparse or delayed sequence submission during early epidemic and at the end of data retrieval, we exclusively involved genomes sampled between 1 March 2020 and 18 June 2022 in our study. Then a bioinformatic pipeline, as reported by our previous study ( 9 ), was applied to the remaining 10,246,539 (88.9%) sequences to extract and annotate all single nucleotide polymorphisms (SNPs) and insertions/deletions (INDELs) for each genome. In consequence, 519,230,825 mutational events from these sequences were exported and labeled with the sampling week.…”

“…A growing body of evidence shows that multiple mutations arising simultaneously in one genome, referred as co-mutation, can be a reliable predictor for viral variant monitoring ( 5 – 9 ). A collection and combination of co-mutation communities resulted from genomic data accumulated over time helps to capture the evolution and transmission patterns of SARS-CoV-2 ( 7 ).…”

COVID-19 Variant Surveillance and Social Determinants in Central Massachusetts: Development Study

“…The impact of using genomic epidemiology to monitor the COVID-19 pandemic has been profound, as it provides unprecedented detail into the appearance and global dissemination of SARS-CoV-2 variants [ 19 - 24 ]. In addition, phylogenetic-epidemiological analysis has enabled the reconstruction of super-spreader events [ 25 ].…”