Speaker: Katia Koelle (Department of Biology, Duke University)
Both in quality and in quantity, genetic data of viral pathogens now rival their incidence data. However, whereas case data are commonly used to shed light on the ecological factors driving disease dynamics, the same cannot yet be said about viral sequence data. Here, I will present two very distinct approaches that seek to improve the link between a virus’s ecological dynamics and its evolutionary dynamics by considering the information residing in viral phylogenies. The first one focuses on understanding the evolutionary dynamics of antigenically variable RNA viruses (e.g., influenza, norovirus) through the use of a probabilistic framework based on a dimensionless number. The second one develops an approach for deriving coalescent rate expressions directly from the structures of common, but complex, epidemiological models. These rates can be used for population parameter inference of neutrally evolving viruses.