<i>Salmonella</i>source attribution in a subtropical state of Australia: capturing environmental reservoirs of infection

Fearnley, Emily; Lal, Aparna; Bates, John; Stafford, Russell; Kirk, Martyn; Glass, Kathryn

doi:10.1017/s0950268818002224

Cited by 11 publications

(13 citation statements)

References 24 publications

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“…Children 0–4 years of age were the most frequently notified age group (458 [24.7%]). Notification rates were higher in warmer months, similar to those for other non-Typhimurium serovars ( 26 ).…”

Section: Resultssupporting

confidence: 71%

Whole-Genome Sequencing ofSalmonellaMississippi and Typhimurium Definitive Type 160, Australia and New Zealand

Ford¹,

Ingle²,

Glass³

et al. 2019

Emerg. Infect. Dis.

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We used phylogenomic and risk factor data on isolates of Salmonella enterica serovars Mississippi and Typhimurium definitive type 160 (DT160) collected from human, animal, and environmental sources to elucidate their epidemiology and disease reservoirs in Australia and New Zealand. Sequence data suggested wild birds as a likely reservoir for DT160; animal and environmental sources varied more for Salmonella Mississippi than for Salmonella Typhimurium. Australia and New Zealand isolates sat in distinct clades for both serovars; the median single-nucleotide polymorphism distance for DT160 was 29 (range 8–66) and for Salmonella Mississippi, 619 (range 565–737). Phylogenomic data identified plausible sources of human infection from wildlife and environmental reservoirs and provided evidence supporting New Zealand–acquired DT160 in a group of travelers returning to Australia. Wider use of real-time whole-genome sequencing in new locations and for other serovars may identify sources and routes of transmission, thereby aiding prevention and control.

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Section: Resultssupporting

confidence: 71%

Whole-Genome Sequencing ofSalmonellaMississippi and Typhimurium Definitive Type 160, Australia and New Zealand

Ford¹,

Ingle²,

Glass³

et al. 2019

Emerg. Infect. Dis.

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“…From 2001 to 2016, egg-based sauces, desserts, sandwich and salads containing raw or lightly cooked eggs were frequently reported to be associated with Salmonella outbreaks; however, the frequency of reported outbreaks related to the other animal-source foods including poultry meat, beef and pork were declined over the time period [138]. Nevertheless, in a recent study, chicken meat is claimed to be associated with 43.7% of the Salmonella cases in QLD [139].…”

Section: Food-related Origin Of Salmonella Infections/outbreaks In Aumentioning

confidence: 99%

Non-Typhoidal Salmonella at the Human-Food-of-Animal-Origin Interface in Australia

Sodagari

Wang

Robertson

et al. 2020

Animals

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Non-typhoidal Salmonella is a major zoonotic pathogen that plays a significant role in foodborne human salmonellosis worldwide through the consumption of contaminated foods, particularly those of animal origin. Despite a considerable reduction in human salmonellosis outbreaks in developed countries, Australia is experiencing a continuous rise of such outbreaks in humans. This review of the literature highlights the reported non-typhoidal Salmonella outbreaks in humans as well as the occurrence of the pathogen in foods from animal sources throughout Australia. Non-typhoidal Salmonella infections from food animals are more often associated with at-risk people, such as immunocompromised and aged people or children. Although several animal-sourced foods were recognised as the catalysts for salmonellosis outbreaks in Australia, egg and egg-based products remained the most implicated foods in the reported outbreaks. This review further highlights the antimicrobial resistance trends of non-typhoidal Salmonella isolates at the human–food interface, with a focus on clinically important antimicrobials in humans, by collating evidence from previous investigations in Australia. The rise in antimicrobial-resistant Salmonella, especially to antimicrobials commonly prescribed to treat human salmonellosis, has become a significant global public health concern. However, the overall prevalence of antimicrobial resistance in Australia is considerably lower than in other parts of the world, particularly in terms of critically important antimicrobials for the treatment of human salmonellosis. The present review adds to our understanding of the global epidemiology of non-typhoidal Salmonella with emphasis on the past few decades in Australia.

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“…The Danish source account has up until now been based on a Bayesian modeling approach that accounted for prevalence of the Salmonella types in the different sources (Hald, Vose, Wegener, & Koupeev, 2004). The types have over the years been defined by serotyping, phage typing, MLVA typing, and resistance profiling (de Knegt et al., 2016; Hald et al., 2004) and the source attribution model has been modified and applied in other countries such as Australia (Fearnley et al., 2018; Glass et al., 2015) and New Zealand (Mullner et al., 2009).…”

Section: Introductionmentioning

confidence: 99%

Application of Whole‐Genome Sequences and Machine Learning in Source Attribution of Salmonella Typhimurium

et al. 2020

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Prevention of the emergence and spread of foodborne diseases is an important prerequisite for the improvement of public health. Source attribution models link sporadic human cases of a specific illness to food sources and animal reservoirs. With the next generation sequencing technology, it is possible to develop novel source attribution models. We investigated the potential of machine learning to predict the animal reservoir from which a bacterial strain isolated from a human salmonellosis case originated based on whole-genome sequencing. Machine learning methods recognize patterns in large and complex data sets and use this knowledge to build models. The model learns patterns associated with genetic variations in bacteria isolated from the different animal reservoirs. We selected different machine learning algorithms to predict sources of human salmonellosis cases and trained the model with Danish Salmonella Typhimurium isolates sampled from broilers (n = 34), cattle (n = 2), ducks (n = 11), layers (n = 4), and pigs (n = 159). Using cgMLST as input features, the model yielded an average accuracy of 0.783 (95% CI: 0.77-0.80) in the source prediction for the random forest and 0.933 (95% CI: 0.92-0.94) for the logit boost algorithm. Logit boost algorithm was most accurate (valid accuracy: 92%, CI: 0.8706-0.9579) and predicted the origin of 81% of the domestic sporadic human salmonellosis cases. The most important source was Danish produced pigs (53%) followed by imported pigs (16%), imported broilers (6%), imported ducks (2%), Danish produced layers (2%), Danish produced cattle and imported cattle (<1%) while 18% was not predicted. Machine learning has potential for improving source attribution modeling based on sequence data. Results of such models can inform risk managers to identify and prioritize food safety interventions.

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Salmonellasource attribution in a subtropical state of Australia: capturing environmental reservoirs of infection

Cited by 11 publications

References 24 publications

Whole-Genome Sequencing ofSalmonellaMississippi and Typhimurium Definitive Type 160, Australia and New Zealand

Whole-Genome Sequencing ofSalmonellaMississippi and Typhimurium Definitive Type 160, Australia and New Zealand

Non-Typhoidal Salmonella at the Human-Food-of-Animal-Origin Interface in Australia

Application of Whole‐Genome Sequences and Machine Learning in Source Attribution of Salmonella Typhimurium

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