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Phylodynamics of infectious disease epidemics
The genetic diversity of many pathogens is shaped by epidemiological history. But, the dynamics of infectious disease epidemics differ in important ways from demographic processes that have traditionally been studied by population geneticists. In many epidemics, the population size and birth rate changes rapidly in a nonlinear fashion through time. Mathematical models for describing infectious disease dynamics have a long history that has run parallel to the development of modern population genetics, but until recently, there has been little communication between these fields.Interest has grown in developing a new set of mathematical models for genealogies generated by epidemic processes. These methods reveal how the effective population size of a pathogen depends on transmission rates, the number of infected hosts, and the size of the bottleneck at the time of transmission. These mathematical models have also enabled new applications of pathogen genetic data to public health. Pathogen genetic data can be informative about epidemic processes in ways that standard surveillance data are not, especially regarding the source of infections and risk factors for transmission. I will review several approaches to mathematical modeling of pathogen genealogies and present applications of these methods to HIV-1 and the recent Ebola virus epidemic in Western Africa.
Statistical inference for phylodynamics
Phylodynamic methods are widely used to estimate demographic parameters and historical population dynamics from genealogies of individuals sampled from a population. In this phyloseminar, I will describe how we can understand genealogies in terms of basic demographic or ecological processes, and how these concepts can be used to develop statistical models for inference. In particular, I will discuss some similarities and differences between the two main modeling frameworks in phylodynamics: the coalescent and birth-death models. I will also briefly introduce some of the latest statistical methods currently used to fit these models to genealogies. I will end by discussing one of the main challenges facing the field---adequately representing the structure of complex, heterogenous populations in phylodynamic models.