Statistical Methods for Integrating epidemiological and whole genome sequence data for effectively analysing infectious disease outbreak data

In the past few years, advances in sequencing technology and the reduction in associated costs have enabled scientists to obtain highly detailed genomic data on disease-causing pathogens on a scale never seen before. In addition to the inherent phylogenetic information contained in such data, combining genomic data with traditional epidemiological data (such as time series of case incidence) also provides an opportunity to perform microbial source attribution, i.e. determining the actual transmission pathway of the pathogen through a population.
Despite the recent advances, existing approaches have their own limitations which can create estimation biases and lead to misleading results.
This project is concerned with:
i) developing models and computationally efficient methods to effectively analyse epidemiological and high-resolution genetic data by extending the approach of Worby et al.(2016)
ii) apply to methods real-data.