Investigation of Viral Transmission Events based on Within-Host Time-Series Data (360G-Wellcome-105365_Z_14_Z)

The main aim of my research is to develop new techniques for investigating transmission events in influenza A virus. These techniques will be mathematical and computational in nature and revolve around the use of publicly available time-resolved within-host data. Specifically, I propose to use maximum likelihood methods to extract information on transmission bottleneck size and selection based on the time-evolution of genetic variants. As a secondary aim, I wish to extend my methods (and perhaps those of other authors) to allow for analysis of other viral species such as HIV, norovirus and picornavirus (foot-and-mouth disease). Influenza virus is particularly convenient to work with as it has a short lifetime, large population size and lacks homologous recombination, however, other RNA viruses are more complicated in these aspects. As a result of this, my initial algorithm may require important adjustments to reflect this. Key goals: ? Incorporation of genetic selection to my model developed during research rotation ? Implementation of Gaussian transmission step in maximum likelihood approach leading to a more tractable algorithm ? Extension of algorithm to allow for analysis of multiple gene segments ? Analysis of existing influenza datasets ? Extension to and analysis of other RNA virus species