The main thrust of this thesis was to characterize P.falciparum genetic diversity in northern Ghana. To do this, I used simple techniques to purify P. falciparum DNA from clinical samples across a rural setting for whole-genome sequencing. The goal was to provide a framework for exploring host-parasite genetic interactions. Utilizing Illumina deep sequencing data for 277 isolates I analyzed P. falciparum genetic diversity and described within-host diversity across this area. I observed random mating (ie no population structure) in the local parasite population, and a high genetic diversity indicative of high out-crossing. Moreover, when I aggregated my data with similar published data from Burkina Faso and Mali (sites ≈500km apart), no population structure was evident. In contrast, sites sampled in Cambodia and Thailand (≈ 800km apart) were found to have greater population structure and high potential for inbreeding. This may be driven by differences in transmission intensity between the sites sampled in West Africa and southeast Asia. To demonstrate the utility of deep sequencing data, I focused on the genomic regions of pfdhfr, pfdhps and pfcrt, known to be under antimalarial drug selection. I surveyed the full diversity of point mutations already characterized in these genes and discovered previously unknown variants. However, in order to provide a means to follow up on new variants or interesting candidate regions in large clinical samples with limited parasite DNA, I assessed the Sequenom iPLEX platform for high-throughput genotyping of P. falciparum polymorphisms. This necessitated developing a method appropriate for assigning genotypes in haploid genome mixtures common in natural infections. Finally, I used this method to type host and parasite markers in a case-control sample set from this region for exploring host-parasite genetic interactions. I found that children who have the sickle-cell trait and carry parasites that have pfdhfr resistant alleles lose their protection against severe malaria as compared to children who have normal haemoglobin and are infected with parasites with these resistant alleles.

As the leading cause of bacterial gastroenteritis, Campylobacter represents a significant public health burden; however, our knowledge of its epidemiology in low- and middle-income countries remains limited. Recent studies have demonstrated the power of whole-genome sequencing (WGS) for public health microbiology. The primary aim of this thesis was to exploit WGS to improve our understanding of the epidemiology of Campylobacter from the Republic of South Africa, a middle-income country. In the first half of this thesis, in silico approaches were developed to evaluate diagnostic assays and methods of species identification. Large-scale analyses of publicly available WGS data identified a robust real-time PCR assay for the detection of Campylobacter jejuni and Campylobacter coli, the primary causes of human campylobacteriosis. Evaluation of in silico speciation methods demonstrated that the atpA gene and ribosomal multilocus sequence typing can be used to identify Campylobacter from WGS data. The second half of this thesis extended concepts developed in the first half to investigate the epidemiology of Campylobacter from animals and humans from South Africa. Isolates from a study of Campylobacter from free-range broiler carcasses belonged to the agriculture-associated ST-828 lineage, but were atypically homogenous and differed at only 46/1,513 (3%) loci, providing novel insights into clonal infections in chickens. Analyses of human disease isolates collected in Cape Town in 1991, 2011, and 2012 confirmed that the local epidemiology of Campylobacter is distinct from that of high-income countries: in addition to major agriculture-associated C. jejuni and C. coli lineages, a putative novel C. jejuni subsp. jejuni/C. jejuni subsp. doylei hybrid clade and genetically diverse C. jejuni subsp. doylei and C. upsaliensis isolates were identified. This work delivers further evidence of the utility of WGS for clinical microbiology, presents approaches that address general problems in Campylobacter diagnostics and public health microbiology, and provides insights into the epidemiology of this important group of pathogens in South Africa.

abstract: The severity of the health and economic devastation resulting from outbreaks of viruses such as Zika, Ebola, SARS-CoV-1 and, most recently, SARS-CoV-2 underscores the need for tools which aim to delineate critical disease dynamical features underlying observed patterns of infectious disease spread. The growing emphasis placed on genome sequencing to support pathogen outbreak response highlights the need to adapt traditional epidemiological metrics to leverage this increasingly rich data stream. Further, the rapidity with which pathogen molecular sequence data is now generated, coupled with advent of sophisticated, Bayesian statistical techniques for pathogen molecular sequence analysis, creates an unprecedented opportunity to disrupt and innovate public health surveillance using 21st century tools. Bayesian phylogeography is a modeling framework which assumes discrete traits -- such as age, location of sampling, or species -- evolve according to a continuous-time Markov chain process along a phylogenetic tree topology which is inferred from molecular sequence data. While myriad studies exist which reconstruct patterns of discrete trait evolution along an inferred phylogeny, attempts to translate the results of phyloegographic analyses into actionable metrics that can be used by public health agencies to direct the development of interventions aimed at reducing pathogen spread are conspicuously absent from the literature. In this dissertation, I focus on developing an intuitive metric, the phylogenetic risk ratio (PRR), which I use to translate the results of Bayesian phylogeographic modeling studies into a form actionable by public health agencies. I apply the PRR to two case studies: i) age-associated diffusion of influenza A/H3N2 during the 2016-17 US epidemic and ii) host associated diffusion of West Nile virus in the US. I discuss the limitations of this (and Bayesian phylogeographic) approaches when studying non-geographic traits for which limited metadata is available in public molecular sequence databases and statistically principled solutions to the missing metadata problem in the phylogenetic context. Then, I perform a simulation study to evaluate the statistical performance of the missing metadata solution. Finally, I provide a solution for researchers whom are interested in using the PRR and phylogenetic UTMs in their own genomic epidemiological studies yet are deterred by the idiosyncratic, error-prone processes required to implement these methods using popular Bayesian phylogenetic inference software packages. My solution, Build-A-BEAST, is a publicly available, object-oriented system written in python which aims to reduce the complexity and idiosyncrasy of creating XML files necessary to perform the aforementioned analyses. This dissertation extends the conceptual framework of Bayesian phylogeographic methods, develops a method to translates the output of phylogenetic models into an actionable form, evaluates the use of priors for missing metadata, and, finally, provides a solution which eases the implementation of these methods. In doing so, I lay the foundation for future work in disseminating and implementing Bayesian phylogeographic methods for routine public health surveillance. / Dissertation/Thesis / Doctoral Dissertation Biomedical Informatics 2020