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Four European Salmonella Typhimurium datasets collected to develop WGS-based source attribution methods

  • Nanna Munck*
  • , Pimlapas Leekitcharoenphon
  • , Eva Litrup
  • , Rolf Kaas
  • , Anika Meinen
  • , Laurent Guillier
  • , Yue Tang
  • , Burkhard Malorny
  • , Federica Palma
  • , Maria Borowiak
  • , Michèle Gourmelon
  • , Sandra Simon
  • , Sangeeta Banerji
  • , Liljana Petrovska
  • , Timothy J Dallman
  • , Tine Hald
  • *Corresponding author for this work
  • Technical University of Denmark
  • Statens Serum Institut
  • Robert Koch Institute
  • Université Paris-Est Créteil
  • Ghent University
  • German Federal Institute for Risk Assessment
  • Institut des Sciences de l'Evolution, UMR 5554, CNRS, Université Montpellier 2, CC 065, Place Eugène Bataillon, 34095 Montpellier, Cedex 05, France.; MARBEC, UMR IRD-CNRS-UM-IFREMER 9190, Université Montpellier, CC 093, FR-34095 Montpellier, Cedex 5, France.
  • Public Health England

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Zoonotic Salmonella causes millions of human salmonellosis infections worldwide each year. Information about the source of the bacteria guides risk managers on control and preventive strategies. Source attribution is the effort to quantify the number of sporadic human cases of a specific illness to specific sources and animal reservoirs. Source attribution methods for Salmonella have so far been based on traditional wet-lab typing methods. With the change to whole genome sequencing there is a need to develop new methods for source attribution based on sequencing data. Four European datasets collected in Denmark (DK), Germany (DE), the United Kingdom (UK) and France (FR) are presented in this descriptor. The datasets contain sequenced samples of Salmonella Typhimurium and its monophasic variants isolated from human, food, animal and the environment. The objective of the datasets was either to attribute the human salmonellosis cases to animal reservoirs or to investigate contamination of the environment by attributing the environmental isolates to different animal reservoirs.

Original languageEnglish
Article number75
Journal Scientific data
Volume7
Issue number1
DOIs
Publication statusPublished - 3 Mar 2020
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Animals
  • Denmark
  • Disease Reservoirs
  • Environmental Microbiology
  • France
  • Germany
  • Humans
  • Salmonella Food Poisoning
  • Salmonella typhimurium/genetics
  • United Kingdom
  • Whole Genome Sequencing
  • Zoonoses/microbiology

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