Analyzing Patterns of COVID spread using computational network analysis
By: Fiona Senchyna
Department: Computer Science
Faculty Advisor: Dr. Rahul Singh
The global COVID-19 pandemic continues to have a devastating impact on human population health. A large quantity of viral samples were (and continue to be) collected across the world as part of public health efforts. Sequencing of these samples has led to the creation of vast quantities of data documenting the molecular evolution of the virus across locations and time. We present methods and results from our research that demonstrate how an abstract mathematical/data structure called a network can be used to represent, model, and find patterns in this data. In our representation, samples are represented by nodes, and edges join samples based on sequence similarity. We present longitudinal analysis of several country specific SARS-CoV-2 datasets and show how changes in network topological properties over time can be correlated with viral evolution and spread.