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Title: How can we use whole genome sequencing and mathematical modelling to understand tuberculosis transmission and inform our public health practices?
Author: Hatherell, Hollie-Ann
ISNI:       0000 0004 8508 2980
Awarding Body: UCL (University College London)
Current Institution: University College London (University of London)
Date of Award: 2020
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Tuberculosis (TB) remains a public health problem in cities in high-income, low-incidence countries, such as London, where it disproportionately affects particular population groups and, as such, more effective intervention strategies are needed. With whole genome sequencing (WGS) data being increasingly used for TB epidemiology, I investigated how WGS data alongside statistical inference and mathematical modelling can improve our understanding of transmission in these population groups. By reviewing the literature on WGS in TB epidemiology studies, I concluded that whilst genomic data can improve our understanding of TB transmission, including epidemiological data alongside is helpful for mitigating uninformative genomic data or strengthening conclusions. I then employed a statistical inference method on sequencing data from a Canadian outbreak and used the inferred transmission network to determine that the outbreak had ended, demonstrating the use of genomic epidemiology in public health. As we must analyse genomic data using bioinformatics and sometimes phylogenetic methods before we can interpret it for epidemiological purposes, I undertook bioinformatics analysis of 415 genomes from a London TB outbreak and attempted to create a timed-phylogenetic tree that could be used for genomic epidemiology inferences. However, the data proved difficult to interpret resulting in a tree with little confidence, potentially due to little variation amongst the sequences. Finally, I constructed a novel mathematical transmission model to recapitulate the London outbreak and investigate public health interventions to conclude that despite loss-to-follow-up being considered an important factor amongst the cohort anecdotally, focusing interventions on reducing loss-to-follow-up or increasing re-engagement does not significantly reduce the number of outbreak cases. Finding infectious cases early achieves the most impact. In conclusion, combining epidemiological and sequencing with novel quantitative analysis using statistical inference and transmission modelling, provides useful insight into the spread of TB in urban outbreaks and illustrates the limitations of new approaches and data.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available