Spatiotemporal Tracking of SARS-CoV-2 Variants using informative subtype markers and association graphs

摘要

Viral subtyping can facilitate visualization and modeling of the geographic distribution and temporal dynamics of disease spread. Understanding the virus's evolution spatiotemporally can help forensic strategies. We have identified mutation variation within SARS-CoV-2 sequences via an entropy measure followed by frequency analysis. These signatures, Informative Subtype Markers (ISMs), define a compact set of nucleotide sites that characterize the most variable (and thus most informative) positions in the viral genomes sequenced from different individuals. Using these ISMs, we show that we can use them for a variety of downstream analyses, such as comparing countries' subtype compositions. We present association graphs as a visualization tool to connect different ISMs based on their co-occurrence across different individuals. In particular, we investigate dominant ISMs for different locations, across different factors such as gender and age.