My group builds methods to infer the transmission dynamics of pathogens with the help of sequence data, and mathematical methods for phylogenetics more broadly. This area includes my work on methods like TransPhylo, which allows researchers to infer transmission trees (who infected whom?) from pathogen sequence data, enhancing our understanding of transmission and ultimately our ability to control outbreaks. Key papers in this area:

We build models and techniques for inference and estimation in infectious disease, often using non-traditional data sources like pathogen genomes, phylogenetic trees, cluster size distributions, contact tracing data, and even, in one case, google images. We combine these with mathematical models to build an understanding of infectious disease transmission and evolution. Key papers in this area:

We create dynamic transmission models, integrating a range of data sources, to simulate interacting infections, characterize the impact of interventions, and to understand how diseases spread and evolve. Our work in modelling was used heavily in the COVID-19 pandemic by provincial and federal public health institutions and other organizations. I have a particular interest in modelling infections with multiple circulating strains, and in exploring inter-strain interactions in the context of evolution. A particularly exciting recent direction is to build methods that link the insights we gain from genomic data to dynamic models that can project what those insights mean for the future trajectory and evolution of a pathogen. Key papers in this area:

We investigate the evolution and spread of vaccine escape strains and antimicrobial resistance using a range of modelling and estimation approaches. This research helps to understand how pathogen populations evolve and how resistance and/or vaccine escape emerges and spreads in pathogen populations, and can inform strategies to mitigate the harms caused by pathogen evolution. Key papers in this area: