Viral Evolution, Selection and Diversity
Meeting Times:
- Monday July 28, 9:00 AM – 5:00 PM
- Tuesday July 29, 9:00 AM – 5:00 PM
- Wednesday July 30, 9:00 AM – 12:00 PM
Classroom: TBA
Module Summary:
This module provides an introduction to modeling and analyzing genetically and phenotypically diverse pathogen populations. Complementary epidemiological, ecological, and evolutionary approaches that examine the generation, maintenance, and turnover of pathogen diversity will be covered.
Prerequisites:
This module assumes some knowledge of the material from Module: Mathematical Models of Infectious Diseases, though not necessarily from taking that module. Programming exercises will be conducted in R; some familiarity would be helpful, but not required.
Module Content:
- Viral genetic and phenotypic diversity
- Fitness-impacting viral phenotypes
- Multi-strain models and the concept of invasion fitness
- Evolution of virulence
- Antigenic evolution and antigenic maps
- Neutral models of genetic evolution
- Kingman’s coalescent and phylogenetics
- Identifying fitness variation in viral phylogenies
- Within-host evolution and scales of selection
Instructors
Katia Koelle, PhD
Professor, Department of Biology, Emory University
I study the ecological, evolutionary, and within-host dynamics of RNA viruses using quantitative approaches. These quantitative approaches include the development and simulation of mathematical models, as well as statistically fitting these models to data. We further incorporate phylogenetic analyses into our studies to characterize observed patterns of viral evolution. My focus is on viruses circulating in humans, particularly SARS-CoV-2, influenza, and norovirus.
Pamela Martinez, PhD
Assistant Professor, Department of Microbiology and Department of Statistics, University of Illinois Urbana-Champaign.
My research focuses on the population dynamics of infectious diseases, particularly the impact of host heterogeneity, pathogen diversity, and social inequality on disease transmission. In our group, we use mathematical and computational tools combined with time series data to better understand the biological and epidemiological mechanisms that drive disease dynamics in endemic settings. We study human pathogens, including malaria, rotavirus, influenza, SARS-CoV-2, and RSV.
Required Software:
- R software
Recommended Reading:
Review articles and primary literature will be provided for additional reading.
