Guzmán Rincón, Laura Marcela (2020) Statistical methods for campylobacter outbreak detection using genomics and epidemiological data. PhD thesis, University of Warwick.

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Abstract

Campylobacter infections are the main bacterial cause of gastroenteritis in the UK, causing an estimated 500 thousand cases per year. Health authorities investigate outbreaks to identify the source, control the spread and understand the cause. Outbreak detection mechanisms are potentially improved by the increasing availability of whole-genome sequence alongside other epidemiological data. However, techniques mixing genomics and other epidemiological factors are still underdeveloped. This project aims to develop and apply outbreak detection methods using surveillance data collected from two regions in the UK. The approaches proposed in this thesis are based on an existing spatial-temporal Bayesian hierarchical model, where cases are labelled as potential outbreaks if they comprise an elevated number of cases compared to the expected sporadic count. The model is adjusted to include genetic data using Gaussian random fields, exploiting the capacity of whole-genome sequencing to discriminate closely related isolates. Moreover, a Markov Chain Monte Carlo algorithm is implemented to obtain the posterior distribution of the model parameters. In particular, a sampling strategy is proposed to improve the convergence of the chain for the parameters describing the Gaussian random field. The project dataset is analysed using a spatial-temporal, a spatial-genetic and a temporal-genetic version of the model, where each version explores different types of outbreaks. The proposed approach demonstrates how to organise genetic sequences into a high-dimensional structure and incorporate them into a Bayesian framework. Also, the MCMC sampling algorithm improves the mixing of the chain to estimate the posterior distribution of the model parameters. Finally, all model versions provide the probability that each reported infection is part of a potential outbreak. Comparing the potential outbreaks found by each model provides insights to estimate the real outbreaks. It also identifies cases that are potentially part of a diffuse real outbreak hard to detect by existing approaches. Despite the capability of the model, it requires predefined outbreak sizes and therefore is not flexible at capturing many shapes. Autocorrelated models are a potential improvement to be explored.

Item Type:	Thesis (PhD)
Subjects:	R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
Library of Congress Subject Headings (LCSH):	Public health surveillance -- Statistical methods, Epidemiology -- Statistical methods, Campylobacter infections -- Early detection, Gastroenteritis -- Early detection, Epidemics -- Great Britain -- Prevention
Official Date:	September 2020
Dates:	Date Event September 2020 UNSPECIFIED
Institution:	University of Warwick
Theses Department:	Mathematics for Real-World Systems Centre for Doctoral Training
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	McCarthy, Noel D. ; Spencer, Simon E. F.
Sponsors:	Engineering and Physical Sciences Research Council ; Public Health England
Format of File:	pdf
Extent:	xiv, 125 leaves : illustrations (some colour), maps (some colour)
Language:	SSsseng

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