The recent years have witnessed a rapid increase in the quantity and quality of
genomic data collected from human and animal pathogens, viruses in particular.
When coupled with mathematical and statistical models, these data allow us to
combine evolutionary theory and epidemiology to understand pathogen dynamics.
While these developments led to important epidemiological questions being tackled,
it also exposed the need for improved analytical methods. In this thesis I employ
modern statistical techniques to address two pressing issues in phylodynamics: (i)
computational tools for Bayesian phylogenetics and (ii) data integration. I detail
the development and testing of new transition kernels for Markov Chain Monte
Carlo (MCMC) for time-calibrated phylogenetics in Chapter 2 and show that an
adaptive kernel leads to improved MCMC performance in terms of mixing for a
range of data sets, in particular for a challenging Ebola virus phylogeny with 1610
taxa/sequences. As a trade-off, I also found that the new adaptive kernels have
longer warm up times in general, suggesting room for improvement. Chapter 3
shows how to apply state-of-the-art techniques to visualise and analyse phylogenetic
space and MCMC for time-calibrated phylogenies, which are crucial to the viral
phylodynamics analysis pipeline. I describe a pipeline for a typical phylodynamic
analysis which includes convergence diagnostics for continuous parameters and in
phylogenetic space, extending existing methods to deal with large time-calibrated
phylogenies. In addition I investigate different representations of phylogenetic space
through multi-dimensional scaling (MDS) or univariate distributions of distances
to a focal tree and show that even for the simplest toy examples phylogenetic
space remains complex and in particular not all metrics lead to desirable or useful
representations. On the data integration front, Chapters 4 and 5 detail the use data
from the 2013-2016 Ebola virus disease (EVD) epidemic in West Africa to show how
one can combine phylogenetic and epidemiological data to tackle epidemiological
questions. I explore the determinants of the Ebola epidemic in Chapter 4 through a
generalised linear model framework coupled with Bayesian stochastic search variable
selection (BSSVS) to assess the relative importance climatic and socio-economic
variables on EVD number of cases. In Chapter 5 I tackle the question of whether
a particular glycoprotein mutation could lead to increased human mortality from
EVD. I show that a principled analysis of the available data that accounts for several
sources of uncertainty as well as shared ancestry between samples does not allow us
to ascertain the presence of such effect of a viral mutation on mortality. Chapter
6 attempts to bring the findings of the thesis together and discuss how the field of
phylodynamics, in special its methodological aspect, might move forward
