Tools and methods for rapid phylogeographic analysis of pathogen genomic surveillance data with application to poliovirus
File(s)
Author(s)
Jorgensen, David
Type
Thesis
Abstract
In recent years, the use of phylogenetic analysis to trace infectious diseases has expanded with the increasing accessibility of genetic sequencing globally. Traditional phylogenetic methods tend to consider evolutionary timescales and can be ill-suited to the rapid evolution and movement of human viruses. This thesis presents and tests methods to overcome issues with, and expand upon, existing phylogenetic techniques as well as analysis pipelines and examples of these methods applied to real-world data. The primary focus of the thesis is the application of quick and simple discrete trait Markov models to wild poliovirus transmission and the introduction of a novel method designed to overcome challenges with sampling rate differences by location with this technique. Additionally, extensions to a widely used Bayesian phylodynamic model of SARS-CoV-2 are presented, allowing for more flexible analysis and the inclusion of additional covariate data. These techniques allow us to discern movement patterns between polio-endemic regions over the past decade and changes in SARS-CoV-2 dynamics over the initial outbreak wave with changing non-pharmaceutical interventions in Saudi Arabia. We present these analyses with a mix of novel and existing visualisation techniques designed to aid their interpretation by key stakeholders and policymakers. The new discrete trait technique presented has the potential to become widely adopted due to the simplicity of the proposed correction and ease of implementation in several existing platforms and the extension of a phylodynamic model of SARS-CoV-2 shows promise for better modelling of future pandemic diseases.
Version
Open Access
Date Issued
2023-04-03
Date Awarded
01/08/2023
Copyright Statement
Creative Commons Attribution NonCommercial Licence
License URL
Advisor
Grassly, Nicholas
Volz, Erik
Sponsor
Engineering and Physical Sciences Research Council
Publisher Department
Medicine
Publisher Institution
Imperial College London
Qualification Level
Doctoral
Qualification Name
Doctor of Philosophy (PhD)