Attributable sources of the five most prevalent non-typhoidal Salmonella serovars across ten European countries

Non-typhoidal Salmonella is the second most frequently reported zoonotic pathogen in the European Union and European Economic Area. Most human infections are caused by serovars Enteritidis and Typhimurium. Genomic characterisation of Salmonella isolates from humans and animals has become a routine public health surveillance tool in many countries. In this study, the relative contributions of several potential sources of human infection of the five frequently reported Salmonella serovars were estimated using machine-learning methods based on a large, cross-sectional collection of genomes from human cases, and animal and environmental sources, across ten European countries. To define the population structure, core-genome Multilocus Sequence Typing was performed. A supervised machine-learning approach was applied for source attribution in the form of a Random Forest classifier. The source and country attribution models achieved moderate accuracy (F1=0.6-0.9), which is lower than in previous studies using machine-learning on Whole Genome Sequencing data. However, attributions of human clinical isolates to different sources were generally in line with previous findings for these five serovars. While the lack of clonality in some sources hindered their prediction, it is also likely that certain sources (e.g., pets) do not serve as major contributors to human infection. Therefore, in most cases attributing these sources to the livestock species they are typically associated with, is likely appropriate. Country attributions showed that substantial human cases are attributable to countries other than their own, indicating geographical interrelatedness of sources. This highlights the value of internationally harmonised Salmonella-control policies in the food production chain.

Authors

Institutions

Publication Details

Journal
Journal of Infection
Published
2025-10-13
DOI
https://doi.org/10.1016/j.jinf.2025.106632
Citations
1
Primary Topic
Salmonella and Campylobacter epidemiology
Type
article
Field-Weighted Citation Impact
1.16

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Attributable sources of the five most prevalent non-typhoidal Salmonella serovars across ten European countries

Leonor Silveira, Robert Söderlund, Kaye Burgess, Magdalena Zając et al.
1 citations
Journal of Infection
Salmonella and Campylobacter epidemiology
1.16
article

Attributable sources of the five most prevalent non-typhoidal Salmonella serovars across ten European countries

Leonor Silveira, Robert Söderlund, Kaye Burgess, Magdalena Zając, Ângela Pista, Lurdes Clemente, Sara M. Pires, Laetitia Bonifait, Marie Anne Chattaway, Liljana Petrovska, Marianne Chemaly, Annaëlle Kérouanton, Amandine Thépault, Julio Álvarez, Eva Møller Nielsen, Magdalena Skarżyńska, Maaike J. C. van den Beld, Tine Hald, Antoni P. A. Hendrickx, Timothy J. Dallman, Eelco Franz, William Byrne, Lapo Mughini-Gras, Gijs Teunis
article en
1 citations

Abstract

Non-typhoidal Salmonella is the second most frequently reported zoonotic pathogen in the European Union and European Economic Area. Most human infections are caused by serovars Enteritidis and Typhimurium. Genomic characterisation of Salmonella isolates from humans and animals has become a routine public health surveillance tool in many countries. In this study, the relative contributions of several potential sources of human infection of the five frequently reported Salmonella serovars were estimated using machine-learning methods based on a large, cross-sectional collection of genomes from human cases, and animal and environmental sources, across ten European countries. To define the population structure, core-genome Multilocus Sequence Typing was performed. A supervised machine-learning approach was applied for source attribution in the form of a Random Forest classifier. The source and country attribution models achieved moderate accuracy (F1=0.6-0.9), which is lower than in previous studies using machine-learning on Whole Genome Sequencing data. However, attributions of human clinical isolates to different sources were generally in line with previous findings for these five serovars. While the lack of clonality in some sources hindered their prediction, it is also likely that certain sources (e.g., pets) do not serve as major contributors to human infection. Therefore, in most cases attributing these sources to the livestock species they are typically associated with, is likely appropriate. Country attributions showed that substantial human cases are attributable to countries other than their own, indicating geographical interrelatedness of sources. This highlights the value of internationally harmonised Salmonella-control policies in the food production chain.

Journal of InfectionVol. 91(5)
Statens Serum Institut (DK), Universidad Complutense de Madrid (ES), Teagasc - The Irish Agriculture and Food Development Authority (IE), Animal and Plant Health Agency (GB), Utrecht University (NL), Public Health England (GB), Department of Agriculture Food and the Marine (IE), National Institute for Public Health and the Environment (NL), Instituto Nacional de Investigação Agrária e Veterinária (PT), National Veterinary Research Institute (PL), Agence Nationale de Sécurité Sanitaire de l’Alimentation, de l’Environnement et du Travail (FR), National Institute of Health Dr. Ricardo Jorge (PT), Technical University of Denmark (DK)
National Institute for Health Research Health Protection Research Unit, National Institute for Health and Care Research, Department of Health and Social Care, University of Warwick, European Commission, Państwowy Instytut Weterynaryjny - Państwowy Instytut Badawczy
Openalex Percentile: Top 17%
Salmonella and Campylobacter epidemiology
1.16
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.