To understand the transmission dynamics and evolutionary patterns of the SARS-CoV-2 virus, researchers in the School of Public Health and department of Computer Science in the College of Arts & Sciences use the methods of phylogenetics.
Viruses mutate extremely rapidly, and accumulate mutations during the process of transmissions between different people. Wide utilization of so-called next-generation sequencing technologies during the COVID-19 pandemic allows laboratories all over the world to sequence and detect new SARS-CoV-2 genomes in almost real time. The result is a snapshot of the evolutionary space explored by the virus from the onset of the epidemic.
Plots of two networks and situation reports accompanying each update will be featured weekly.
The trees and networks are constructed using the sequencing data available through GISAID database. The researchers gratefully acknowledge the researchers and laboratories that produced the data. A full listing of all originating laboratories and authors is available here.
VIRUS TRANSMISSION NETWORK #1
A transmission network is constructed as the union of all minimum spanning trees of the complete weighted graph with vertices being aligned SARS-CoV-2 genomes and edge weights being Hamming distances between them. The network was bootstrapped by uniform sampling of the alignment columns, and the edges with high enough bootstrap probabilities are reported.
The figure depicts the transmission network constructed using SARS-CoV-2 genomes as reported by April 5, 2020. On this transmission network, vertices represent viral genomes, and two vertices are connected by an arc if their mutational composition suggests potential direct or indirect transmission linkage between their hosts. Each vertex is annotated by the list of geographical locations were it was sampled. If a vertex is not annotated, it is sampled at the same location as its first annotated ancestor. This network was visualized using Gephi.
Click on the figure to expand, zoom in, and view the full details of the transmission network. Please view on a desktop for optimum visualization.
VIRUS TRANSMISSION NETWORK #2
A transmission network is constructed as the union of all minimum spanning trees of the complete weighted graph with vertices being aligned SARS-CoV-2 genomes and edge weights being Hamming distances between them. The network was bootstrapped by uniform sampling of the alignment columns, and the edges with high enough bootstrap probabilities are reported.
The figure depicts the transmission network constructed using SARS-CoV-2 genomes as reported by April 30, 2020. On this transmission network, subscriptions are replaced by the colors indicating the geographic locations. This network was visualized using Gephi.
Click on the figure to expand, zoom in, and view the full details of the transmission network. Please view on a desktop for optimum visualization.
ARCHIVED FIGURES AND FORECASTS
The so-called character-based phylogenetic tree with repeated mutations under Camin-Sokal model (or mutation tree for short) is the basic data structure. Based on the mutation tree, a mutation network has been inferred.
This figure depicts the mutation tree of SARS-CoV-2 virus as of March 10, 2020. The nodes represent mutations, each node is annotated by the gene where this mutationoccur, and non-synonymous mutations are highlighted in red.
FEATURED RESEARCH: CORONAVIRUS INCIDENCE FORECASTS
Researchers in the School of Public Health produce daily forecasts of global cases and deaths of COVID-19, the novel coronavirus.
Learn more: http://publichealth.gsu.edu/coronavirus
SCHOLARLY/ACADEMIC RESOURCES
Global transmission network of SARS-CoV-2: from outbreak to pandemic
MEDIA/NEWS REPORTS
FEATURED RESEARCHERS
Dr. Pavel Skums
Dr. Pavel Skums is an assistant professor of computer science in the College of Arts & Sciences. His research areas are computational genomics, molecular epidemiology and network science, with the particular emphasis on design and applications of algorithms for inference and analysis of epidemiological and evolutionary dynamics of viruses.
Before joining Georgia State University, Dr. Skums was an associate service fellow at the Centers for Disease Control and Prevention.
Dr. Gerardo Chowell
Dr. Gerardo Chowell is professor of mathematical epidemiology and chair of the Department of Population Health Sciences. He also holds an external affiliation as a Senior Research Fellow at the Division of International Epidemiology and Population Studies at the Fogarty International Center, National Institutes of Health.
Before joining Georgia State University’s School of Public Health, Dr. Chowell was an associate professor in the School of Human Evolution and Social Change at Arizona State University.
Learn more about Dr. Gerardo Chowell
Contact: gchowell@gsu.edu
Pelin Icer Baykal
Pelin Icer Baykal is a doctoral student and graduate research assistant in the Department of Computer Science in the College of Arts & Sciences. She is a Molecular Basis of Disease (MBD) fellow. Her current research focuses on the prediction of infection stage of viral diseases and their transmission/infection paths.
Her research interests include bioinformatics, machine learning and its applications in disease research.
Contact: picer1@gsu.edu
Sergey Knyazev
Sergey Knyazev is a doctoral student and graduate research assistant in the Department of Computer Science in the College of Arts & Sciences. He is a Molecular Basis of Disease (MBD) fellow and Oak Ridge Institute for Science and Education (ORISE) fellow. His current research focuses on the methods for discovering of intra-host and inter-host viral populations.
His research interests include bioinformatics, graph algorithms, and viral outbreak investigations.
Contact: sergey.n.knyazev@gmail.com
Fatemeh Mohebbi
Fatemeh Mohebbi is a doctoral student and graduate research assistant in the Department of Computer Science in the College of Arts & Sciences. Her current research focuses on the reconstruction of disease transmission networks of viral outbreaks.
Her research interests include bioinformatics and graph algorithm
Contact: fmohebbi1@gsu.edu