Quantifying SARS-CoV-2 and mpox transmission patterns through phylodynamic inference

Abstract

Emerging infectious diseases represent an urgent public health challenge. Unequal coverage of public health surveillance as well as asymptomatic spread, however, limit our ability to respond to outbreaks in a precise and timely manner. In this dissertation, I describe how genomic epidemiology can aid traditional public health investigations of emerging infectious disease dynamics. I begin by describing how matching epidemiological and genomic data from a genomic surveillance system allows for the quantification of variant-specific effects of SARS-CoV-2 infection on the risk of hospitalization, and how vaccination modifies that risk. The subsequent chapters represent phylodynamic studies of mpox and SARS-CoV-2, which show how incorporating epidemiological and mobility information into phylodynamic analyses allows for more precise examination of within- and between-region transmission dynamics, both on a global and local scale. I use these phylodynamic models to investigate the impact of infection control measures, such as stay-at-home orders or vaccination campaigns, on curbing disease spread. Collectively, this dissertation highlights the utility of robustly joining epidemiological and genomic data to augment outbreak response, especially in support of marginalized communities that are especially vulnerable to emerging pathogens.