2023 147 CSM

Identification and Tracking of SARS-CoV-2 Variants with Dynamic Networks

By: Fiona Senchyna

Department: Computer Science

Faculty Advisor: Dr. Rahul Singh

Phylogenetic trees are the current standard for identification of viral variants through genomic data. However, phylogenetic analysis is static and intrinsically batch mode in nature, requiring the presence of the entire data set. Thus, it is not ideal in cases such as COVID-19, where the etiological agent, SARS-CoV-2, is rapidly evolving, and variant identification and tracking must be done in real time. This project describes a novel data representation framework, called Dynamic Epidemiological Networks (DEN), along with algorithms that underpin its construction. The proposed representation is applied to study the molecular development underlying the spread of the COVID-19 pandemic, and the results demonstrate how this framework could be used to provide a multiscale representation of the data by capturing molecular relationships between samples as well as those between variants, automatically identifying the emergence of high frequency variants, including variants of concern such as Alpha and Delta, and tracking their growth. Additionally, we show how analyzing the evolution of the DEN can help identify changes in the viral population that could not be readily inferred from phylogenetic analysis.