University of California, San Diego

Phylogenetic analysis has played a crucial role in increasing our understanding of many aspects of viral and bacterial pathogen biology. Recent advances in evolutionary analysis of sequence data, including time- stamped data and deep sequencing, have allowed quantitative description of epidemic structure for viruses like HIV and HCV. Phylogenetic approaches applied to local datasets of viral sequences with high density coverage of the target population, both from research cohorts and routine clinical care, have been used in studies of transmission correlates, vaccine efficacy evaluation, and various aspects of public health. The non- random structure of the underlying transmission network, its role in the epidemic and its implications for treatment and prevention can now be inferred from sequence data and modeled. In this project, we will develop more innovative models of pathogen transmission combining population genetics, sequence evolution, and network theory, provide efficient method implementation and fast approximate algorithms scalable to global-scale datasets, evaluate the effect of prevention and treatment approaches on epidemic dynamics in five localized epidemics of HIV and HCV, and model generalized epidemics for these and other pathogens. By developing computational and statistical methods that incorporate and analyze pathogen sequence and other epidemiologic data, we will be able to infer and characterize transmission networks to best identify targets for the most effective and parsimonious use of prevention interventions.