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|Organisation:||Imperial College London|
|Tags:||Fellowship: Previous Fellow, Imperial College London, Researcher|
|Related theme:||Healthcare technologies Mathematical sciences|
I studied mathematics and physics at UBC and did a PhD in Applied Mathematics at the University of Waterloo. I did postdoctoral work at McGill and Harvard, where I became interested in infectious disease. I noted that key challenges in infectious disease are not well answered by the standard models people use in epidemiology, and became interested in pathogen diversity -- both from the point of view of multiple interacting strains, and in understanding diversity using sequence data.
This proposal aims to improve our ability to infer the ecological processes shaping a pathogen's evolution by understanding pathogen phylogenies in a novel way. Ecology has a lot to offer for our understanding of pathogens: for example, how and why some pathogens evolve widespread drug resistance rapidly while others maintain long-term coexistence of resistant and sensitive strains is fundamentally an ecological question.
Pathogen phylogenies contain a lot of information about the specifics of where certain strains or sequences originate and about the underlying processes shaping when, where, and which pathogen strains are able to spread. Mathematicians have developed tools to infer phylogenetic trees representing the estimated ancestral patterns of a dataset, as well as tools to simultaneously infer a phylogeny and the population's demographic history. However, existing tools offer no systematic approaches to infer the ecological context shaping a pathogen's spread or evolution. In addition, current tools to compare phylogenies to each other are based on very little of the rich information in genetic data. The work proposed here aims to fill the gap between the rich datasets of pathogen genomes being gathered and our ability to analyse them.
Next generation sequencing provides an unprecedented opportunity to study pathogen evolution in detail and at high resolution. Infectious diseases are an important challenge facing society today. In this project we seek to develop the theoretical models and statistical tools to answer ecological questions about pathogens from novel data.