Literature DB >> 27002674

Application of Molecular Typing Results in Source Attribution Models: The Case of Multiple Locus Variable Number Tandem Repeat Analysis (MLVA) of Salmonella Isolates Obtained from Integrated Surveillance in Denmark.

Leonardo V de Knegt1, Sara M Pires2, Charlotta Löfström3, Gitte Sørensen3, Karl Pedersen4, Mia Torpdahl5, Eva M Nielsen5, Tine Hald1.   

Abstract

Salmonella is an important cause of bacterial foodborne infections in Denmark. To identify the main animal-food sources of human salmonellosis, risk managers have relied on a routine application of a microbial subtyping-based source attribution model since 1995. In 2013, multiple locus variable number tandem repeat analysis (MLVA) substituted phage typing as the subtyping method for surveillance of S. Enteritidis and S. Typhimurium isolated from animals, food, and humans in Denmark. The purpose of this study was to develop a modeling approach applying a combination of serovars, MLVA types, and antibiotic resistance profiles for the Salmonella source attribution, and assess the utility of the results for the food safety decisionmakers. Full and simplified MLVA schemes from surveillance data were tested, and model fit and consistency of results were assessed using statistical measures. We conclude that loci schemes STTR5/STTR10/STTR3 for S. Typhimurium and SE9/SE5/SE2/SE1/SE3 for S. Enteritidis can be used in microbial subtyping-based source attribution models. Based on the results, we discuss that an adjustment of the discriminatory level of the subtyping method applied often will be required to fit the purpose of the study and the available data. The issues discussed are also considered highly relevant when applying, e.g., extended multi-locus sequence typing or next-generation sequencing techniques.
© 2015 Society for Risk Analysis.

Entities:  

Keywords:  Bayesian inference; MLVA; Salmonella; source attribution; surveillance

Mesh:

Year:  2016        PMID: 27002674     DOI: 10.1111/risa.12483

Source DB:  PubMed          Journal:  Risk Anal        ISSN: 0272-4332            Impact factor:   4.000


  9 in total

Review 1.  Microbial source tracking using metagenomics and other new technologies.

Authors:  Shahbaz Raza; Jungman Kim; Michael J Sadowsky; Tatsuya Unno
Journal:  J Microbiol       Date:  2021-02-10       Impact factor: 3.422

2.  Towards an integrated food safety surveillance system: a simulation study to explore the potential of combining genomic and epidemiological metadata.

Authors:  A A Hill; M Crotta; B Wall; L Good; S J O'Brien; J Guitian
Journal:  R Soc Open Sci       Date:  2017-03-29       Impact factor: 2.963

3.  Source Attribution of Salmonella in Macadamia Nuts to Animal and Environmental Reservoirs in Queensland, Australia.

Authors:  Nanna Munck; James Smith; John Bates; Kathryn Glass; Tine Hald; Martyn D Kirk
Journal:  Foodborne Pathog Dis       Date:  2019-12-04       Impact factor: 3.171

4.  Source Attribution of Foodborne Diseases: Potentialities, Hurdles, and Future Expectations.

Authors:  Lapo Mughini-Gras; Pauline Kooh; Jean-Christophe Augustin; Julie David; Philippe Fravalo; Laurent Guillier; Nathalie Jourdan-Da-Silva; Anne Thébault; Moez Sanaa; Laurence Watier
Journal:  Front Microbiol       Date:  2018-09-03       Impact factor: 5.640

5.  Interpretation of Whole-Genome Sequencing for Enteric Disease Surveillance and Outbreak Investigation.

Authors:  John M Besser; Heather A Carleton; Eija Trees; Steven G Stroika; Kelley Hise; Matthew Wise; Peter Gerner-Smidt
Journal:  Foodborne Pathog Dis       Date:  2019-06-27       Impact factor: 3.171

6.  Bayesian Source Attribution of Salmonella Typhimurium Isolates From Human Patients and Farm Animals in England and Wales.

Authors:  Mark Arnold; Richard Piers Smith; Yue Tang; Jaromir Guzinski; Liljana Petrovska
Journal:  Front Microbiol       Date:  2021-01-28       Impact factor: 5.640

7.  A Machine Learning Model for Food Source Attribution of Listeria monocytogenes.

Authors:  Collins K Tanui; Edmund O Benefo; Shraddha Karanth; Abani K Pradhan
Journal:  Pathogens       Date:  2022-06-16

Review 8.  Critical Orientation in the Jungle of Currently Available Methods and Types of Data for Source Attribution of Foodborne Diseases.

Authors:  Lapo Mughini-Gras; Pauline Kooh; Philippe Fravalo; Jean-Christophe Augustin; Laurent Guillier; Julie David; Anne Thébault; Frederic Carlin; Alexandre Leclercq; Nathalie Jourdan-Da-Silva; Nicole Pavio; Isabelle Villena; Moez Sanaa; Laurence Watier
Journal:  Front Microbiol       Date:  2019-11-12       Impact factor: 5.640

9.  Application of Whole-Genome Sequences and Machine Learning in Source Attribution of Salmonella Typhimurium.

Authors:  Nanna Munck; Patrick Murigu Kamau Njage; Pimlapas Leekitcharoenphon; Eva Litrup; Tine Hald
Journal:  Risk Anal       Date:  2020-06-08       Impact factor: 4.000

  9 in total

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