Pathogen phylogenies for public health

Abstract

An organism’s genome sequence is a rich source of information on its current characteristics and its evolutionary history. In this thesis, I refine and apply methods to extract information from pathogen genome sequences via phylogenetic reconstructions. I extend existing phylogeny-based models to new applications in genome-wide association studies (GWAS) and genomic epidemiology. First, I show that correlations in an infectious disease trait due to shared pathogen ancestry can reduce GWAS power. I extend a statistical model of evolution to estimate and correct for these correlations. Second, I apply a phylodynamic model to estimate the origin and early transmission patterns of the SARS-CoV-2 virus during the first European outbreaks of COVID-19. Third, I describe a data infrastructure we built to generate SARS-CoV-2 genome sequences from cases in Switzerland. Finally, I develop a phylogenetic and phylodynamic framework to perform a large-scale analysis on these data. In particular, I evaluate the effect of several major public health measures in Switzerland in 2020 on SARS-CoV-2 introduction and transmission dynamics. All together, this thesis aims to enhance our understanding of infectious diseases and how to combat them.