What While central to public health, the evolution of disease-causing organisms — pathogens — remains highly unpredictable. This project aims to increase our understanding of the driving forces behind pathogen evolution. Parsimonious, data-driven mathematical models of evolution can allow us to probe counter-factual scenarios and assess the impact of individual contributing factors, such as population structure, complex immunity landscapes and differences in how the pathogen evolves within each host. A strong, computational understanding of these processes will allow us to address fundamental questions for disease control as well as vaccine design and distribution. The focus will initially be on SARS-CoV-2 (the virus behind COVID-19), and to a lesser extent influenza, due to the enormity of available data. Longer term, this work may lay the groundwork for cross-pathogen comparisons of evolutionary patterns – i.e., comparative phylodynamics. Why The death toll due to infectious diseases is almost unimaginably high. In a typical year, it is on the order of 10 million deaths and during pandemics it can be substantially higher. The loss of quality of life is likewise enormous. Much of the difficulty of controlling infectious diseases stems from the ability of pathogens to evolve and evade our defenses, whether they are due to acquired immunity, vaccination, medication or even non-pharmaceutical interventions. The good news is that the possibilities for deciphering the evolutionary patterns of pathogens have never been better. The number of available viral genomes has increased radically in recent years, and especially SARS-CoV-2 can serve as a paradigm within which to test evolutionary hypotheses. Doing so will require the formulation of novel data analysis methods as well as model algorithms with which to test the influence of different factors on the evolutionary course of a pathogen. How The methods of statistical physics were developed to describe systems with many – often interacting – constituents. As such they are naturally adaptable to studying the spread of pathogens among a large number of individual hosts. Combining these methods with principles of mathematical epidemiology and large-scale genomic sequence analysis opens up rich possibilities for uncovering the evolutionary patterns of pathogens, as they interact with population immunity. Together with collaborators at Princeton University – the birthplace of the field of phylodynamics – I have already applied these principles to obtain fundamental insights into the evolution of SARS-CoV-2. With this fellowship, I will develop a framework which unifies disease transmission and sequence-level computational modeling to better predict and characterize possible evolutionary shifts for some of the most burdensome pathogens.