A Supervised Statistical Learning Approach for Accurate Legionella pneumophila Source Attribution during Outbreaks.

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, which involved 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' disease 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 a 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.IMPORTANCE Microbial outbreak investigations are moving to a paradigm where whole-genome sequencing and phylogenetic trees are used to support epidemiological investigations. It is critical that outbreak source predictions are accurate, particularly for pathogens, like Legionella pneumophila, which can spread widely and rapidly via cooling system aerosols, causing Legionnaires' disease. Here, by studying hundreds of Legionella pneumophila genomes collected over 21 years around a major Australian city, we uncovered limitations with the phylogenetic approach that could lead to a misidentification of outbreak sources. We implement instead a statistical learning technique that eliminates the ambiguity of inferring disease transmission from phylogenies. Our approach takes geolocation information and core genome variation from environmental L. pneumophila isolates to build statistical models that predict with high confidence the environmental source of clinical L. pneumophila during disease outbreaks. We show the versatility of the technique by applying it to unrelated Legionnaires' disease outbreaks in Australia and the UK.

An outbreak of foodborne norovirus gastroenteritis linked to a restaurant in Melbourne, Australia, 2014.

INTRODUCTION: In May 2014 an outbreak of norovirus occurred among patrons of a restaurant in Melbourne, Australia. Investigations were conducted to identify the infectious agent, mode of transmission and source of illness, and to implement controls to prevent further transmission. METHODS: A retrospective case-control study was conducted to test the hypothesis that food served at the restaurant between 9 and 15 May 2014 was the vehicle for infection. A structured questionnaire was used to collect demographic, illness and food exposure data from study participants. To ascertain whether any food handlers had experienced gastroenteritis symptoms and were a possible source of infection, investigators contacted and interviewed staff who had worked at the restaurant between 9 and 16 May 2014. RESULTS: Forty-six cases (including 16 laboratory-confirmed cases of norovirus) and 49 controls were interviewed and enrolled in the study. Results of the analysis revealed a statistically significant association with illness and consumption of grain salad (OR: 21.6, 95% CI: 1.8-252.7, P = 0.015) and beetroot dip (OR: 22.4, 95% CI: 1.9-267.0, P = 0.014). An interviewed staff member who reported an onset of acute gastrointestinal illness on 12 May 2014 had prepared salads on the day of onset and the previous two days. DISCUSSION: The outbreak was likely caused by person-to-food-to-person transmission. The outbreak emphasizes the importance of the exclusion of symptomatic food handlers and strict hand hygiene practices in the food service industry to prevent contamination of ready-to-eat foods and the kitchen environment.

Salmonella Typhimurium and Salmonella Sofia: Growth in and Persistence on Eggs under Production and Retail Conditions.

Salmonellosis in Australia has been linked to eggs and egg products with specific serotypes associated with outbreaks. We compared attachment to and survival on egg shells and growth in eggs of two Salmonella serotypes, an egg outbreak associated Salmonella Typhimurium and a non-egg-associated Salmonella enterica ssp. II 1,4,12,27:b:[e,n,x] (S. Sofia). Experiments were conducted at combinations of 4, 15, 22, 37 and 42 °C. No significant differences occurred between the serotypes in maximum growth rates, which were significantly greater (P < 0.001) in egg yolk (0.427 log10 CFU/mL/h) compared to whole egg (0.312 log10 CFU/mL/h) and egg white (0.029 log10 CFU/mL/h). Attachment to egg shells varied by time (1 or 20 min) and temperature (4, 22 and 42 °C), with S. Typhimurium isolates attaching at higher levels (P < 0.05) than S. Sofia after 1 min at 4 °C and S. Typhimurium ATCC 14028 attaching at higher (P < 0.05) levels at 22 °C. Survival on egg shells was not significantly different across isolates. Salmonella serotypes behaved similarly regarding growth in egg contents, attachment to egg shells and survival on eggs, indicating that other factors more likely contributed to reasons for S. Typhimurium being implicated in multiple egg-associated outbreaks.