The space of evolutionary trees in infectious disease epidemiology

Summary This project has three main aims; (i) estimating evolutionary parameters for a range of pathogens, such as HIV, influenza, and dengue, (ii) using these parameters to simulate evolutionary trees under different epidemiological models, and (ii) applying machine learning techniques to identify whether epidemiological processes lead to a predictable patterns of evolutionary trees.

Project Details

Infectious disease epidemiology relies on mathematical models that describe epidemiological dynamics. These models typically consider the rate at which individuals are infected and recover from an infection. Because many infectious microbes, such as viruses and bacteria, evolve orders of magnitude faster than their hosts, their genomes have a signature of the transmission process. This project will pinpoint the key differences between evolutionary trees generated under different epidemiological processes. This project has three main aims;

1. estimating evolutionary parameters for a range of pathogens, such as HIV, influenza, and dengue,
2. using these parameters to simulate evolutionary trees under different epidemiological models, and
3. applying machine learning techniques to identify whether epidemiological processes lead to a predictable patterns of evolutionary trees. The outcome will be a framework to classify evolutionary trees from genome surveillance studies, which will contribute to current efforts to couple genomics and epidemiology.