Transmission dynamics of Avian Influenza A virus

Influenza A virus (AIV) has an extremely high rate of mutation. Frequent exchanges of gene segments between different AIV (reassortment) have been responsible for major pandemics in recent human history. The presence of a wild bird reservoir maintains the threat of incursion of AIV into domestic birds, humans and other animals. In this thesis, I addressed unanswered questions of how diverse AIV subtypes (classified according to antigenicity of the two surface proteins, haemagglutinin and neuraminidase) evolve and interact among different bird populations in different parts of the world, using Bayesian phylogenetic methods with large datasets of full genome sequences. Firstly, I explored the reassortment patterns of AIV internal segments among different subtypes by quantifying evolutionary parameters including reassortment rate, evolutionary rate and selective constraint in time-resolved Bayesian tree phylogenies. A major conclusion was that reassortment rate is negatively associated with selective constraint and that infection of wild rather than domestic birds was associated with a higher reassortment rate. Secondly, I described the spatial transmission pattern of AIV in China. Clustering of related viruses in particular geographic areas and economic zones was identified from the viral phylogeographic diffusion networks. The results indicated that Central China and the Pearl River Delta are two main sources of viral out flow; while the East Coast, especially the Yangtze River delta, is the major recipient area. Simultaneously, by applying a general linear model, the predictors that have the strongest impact on viral spatial diffusion were identified, including economic (agricultural) activity, climate, and ecology. Thirdly, I determined the genetic and phylogeographic origin of a recent H7N3 highly pathogenic avian influenza outbreak in Mexico. Location, subtype, avian host species and pathogenicity were modelled as discrete traits and jointly analysed using all eight viral gene segments. The results indicated that the outbreak AIV is a novel reassortant carried by wild waterfowl from different migration flyways in North America during the time period studied. Importantly, I concluded that Mexico, and Central America in general, might be a potential hotspot for AIV reassortment events, a possibility which to date has not attracted widespread attention. Overall, the work carried out in this thesis described the evolutionary dynamics of AIV from which important conclusions regarding its epidemiological impact in both Eurasia and North America can be drawn

Generalized Linear Models in Bayesian Phylogeography

abstract: Bayesian phylogeography is a framework that has enabled researchers to model the spatiotemporal diffusion of pathogens. In general, the framework assumes that discrete geographic sampling traits follow a continuous-time Markov chain process along the branches of an unknown phylogeny that is informed through nucleotide sequence data. Recently, this framework has been extended to model the transition rate matrix between discrete states as a generalized linear model (GLM) of predictors of interest to the pathogen. In this dissertation, I focus on these GLMs and describe their capabilities, limitations, and introduce a pipeline that may enable more researchers to utilize this framework. I first demonstrate how a GLM can be employed and how the support for the predictors can be measured using influenza A/H5N1 in Egypt as an example. Secondly, I compare the GLM framework to two alternative frameworks of Bayesian phylogeography: one that uses an advanced computational technique and one that does not. For this assessment, I model the diffusion of influenza A/H3N2 in the United States during the 2014-15 flu season with five methods encapsulated by the three frameworks. I summarize metrics of the phylogenies created by each and demonstrate their reproducibility by performing analyses on several random sequence samples under a variety of population growth scenarios. Next, I demonstrate how discretization of the location trait for a given sequence set can influence phylogenies and support for predictors. That is, I perform several GLM analyses on a set of sequences and change how the sequences are pooled, then show how aggregating predictors at four levels of spatial resolution will alter posterior support. Finally, I provide a solution for researchers that wish to use the GLM framework but may be deterred by the tedious file-manipulation requirements that must be completed to do so. My pipeline, which is publicly available, should alleviate concerns pertaining to the difficulty and time-consuming nature of creating the files necessary to perform GLM analyses. This dissertation expands the knowledge of Bayesian phylogeographic GLMs and will facilitate the use of this framework, which may ultimately reveal the variables that drive the spread of pathogens.Dissertation/ThesisDoctoral Dissertation Biomedical Informatics 201