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In this cluster-randomized trial conducted in Indonesia, deployment of mosquitoes infected with the w Mel strain of Wolbachia pipientis resulted in fewer symptomatic, virologically confirmed dengue infections and hospitalizations among residents.

The Health Protection Branch of the Victorian Government Department of Health and Human Services monitors and responds to incidents that could adversely affect the health of Victorians. During 2014–2015, I completed a field placement with the branch, assisting with numerous public health investigations and responses. In doing so I fulfilled the requirements of the Master of Philosophy in Applied Epidemiology (MAE). The skills I gained are demonstrated in this thesis. Evaluation of a public health surveillance system is a core requirement for the MAE program. I evaluated Victoria's surveillance and response to legionellosis, which includes both disease surveillance and environmental surveillance and response arms. I found little evidence to support the current practice of sampling and disinfecting cooling towers around the home and workplace for sporadic cases. Improved co-ordination between databases and strategic use of spatial software could help develop more targeted and useful approaches in the future. I embarked on two epidemiological projects. I designed a cross sectional study examining the prevalence of Legionella in domestic potable water and developed participant resources including letters to explain results, meeting the MAE requirement to communicate findings to a non-scientific audience. The study was not completed due to legal considerations; however the proposal and relevant participant resources are included as an appendix. I completed an epidemiological project estimating the number of notified sporadic Salmonella Typhimurium 9 Phage type 9 cases likely to be associated with a recurrent outbreak source during a five year period. I examined 301 clinical Salmonella isolates, including sporadic and outbreak isolates from a series of linked outbreaks, and used multi-locus variable number tandem repeat analysis and whole genome sequence results to estimate the number of isolates genetically linked to the outbreak strain. Outbreak cases accounted for just one third of all isolates estimated to be closely related to the main outbreak clade. This project inspired my lesson from the field, in which I taught MAE colleagues how to analyse MLVA data. I investigated an outbreak of Salmonella Typhimurium phage type 44 at a school function. I conducted a cohort study and interviewed twenty-nine out of thirty guests, of which ten were affected. Roast beef appetiser was the most likely food vehicle for Salmonella infection. Cross-contamination from raw eggs during preparation was a possible source. I analysed a public health dataset to assist a public health investigation into suspected antimony exposure in a rural mining town in Victoria. Residents were concerned about potential health effects from exposure to antimony dust from a local mine. Many sought urinary antimony testing to quantify exposure, with numerous elevated results. I used multivariate regression to examine risk factors for elevated urinary antimony and demonstrated residential proximity to the mine was not associated with urinary antimony results. Overwhelmingly, the largest risk factor for elevated results was the month of testing, consistent with false positive laboratory reports. This thesis documents my experience and capabilities gained during the MAE program, and demonstrates my contribution to protecting the public health of Victorians.

Public health agencies are increasingly relying on genomics during Legionnaires' disease investigations. However, the causative bacterium (Legionella pneumophila) has an unusual population structure with extreme temporal and spatial genome sequence conservation. Furthermore, Legionnaires' disease outbreaks can be caused by multiple L. pneumophila genotypes in a single source. These factors can confound cluster identification using standard phylogenomic methods. Here, we show that a statistical learning approach based on L. pneumophila core genome single nucleotide polymorphism (SNP) comparisons eliminates ambiguity for defining outbreak clusters and accurately predicts exposure sources for clinical cases. We illustrate the performance of our method by genome comparisons of 234 L. pneumophila isolates obtained from patients and cooling towers in Melbourne, Australia between 1994 and 2014. This collection included one of the largest reported Legionnaires' disease outbreaks, involving 125 cases at an aquarium. Using only sequence data from L. pneumophila cooling tower isolates and including all core genome variation, we built a multivariate model using discriminant analysis of principal components (DAPC) to find cooling tower-specific genomic signatures, and then used it to predict the origin of clinical isolates. Model assignments were 93% congruent with epidemiological data, including the aquarium Legionnaires' outbreak and three other unrelated outbreak investigations. We applied the same approach to a recently described investigation of Legionnaires' disease within a UK hospital and observed model predictive ability of 86%. We have developed a promising means to breach L. pneumophila genetic diversity extremes and provide objective source attribution data for outbreak investigations.