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Source attribution of human infection: a multi-country model in the European Union

2025-02-05Journal article Open access
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dc.contributor.authorThystrup, Cecilie
dc.contributor.authorBrinch, Maja Lykke
dc.contributor.authorHenri, Clementine
dc.contributor.authorMughini-Gras, Lapo
dc.contributor.authorFranz, Eelco
dc.contributor.authorWieczorek, Kinga
dc.contributor.authorGutierrez, Montserrat
dc.contributor.authorPrendergast, Deirdre M.
dc.contributor.authorDuffy, Geraldine
dc.contributor.authorBurgess, Catherine M.
dc.contributor.authorBolton, Declan
dc.contributor.authorAlvarez, Julio
dc.contributor.authorLopez-Chavarrias, Vicente
dc.contributor.authorRosendal, Thomas
dc.contributor.authorClemente, Lurdes
dc.contributor.authorAmaro, Ana
dc.contributor.authorZomer, Aldert L.
dc.contributor.authorJoensen, Katrine Grimstrup
dc.contributor.authorNielsen, Eva Møller
dc.contributor.authorScavia, Gaia
dc.contributor.authorSkarżyńska, Magdalena
dc.contributor.authorPinto, Miguel
dc.contributor.authorOleastro, Mónica
dc.contributor.authorCha, Wonhee
dc.contributor.authorThépault, Amandine
dc.contributor.authorRivoal, Katell
dc.contributor.authorDenis, Martine
dc.contributor.authorChemaly, Marianne
dc.contributor.authorHald, Tine
dc.date.accessioned2025-11-14T15:27:04Z
dc.date.available2025-11-14T15:27:04Z
dc.date.issued2025-02-05
dc.description.abstractIntroduction: Infections caused by spp. represent a severe threat to public health worldwide. National action plans have included source attribution studies as a way to quantify the contribution of specific sources and understand the dynamic of transmission of foodborne pathogens like and . Such information is crucial for implementing targeted intervention. The aim of this study was to predict the sources of human campylobacteriosis cases across multiple countries using available whole-genome sequencing (WGS) data and explore the impact of data availability and sample size distribution in a multi-country source attribution model. Methods: We constructed a machine-learning model using -mer frequency patterns as input data to predict human campylobacteriosis cases per source. We then constructed a multi-country model based on data from all countries. Results using different sampling strategies were compared to assess the impact of unbalanced datasets on the prediction of the cases. Results: The results showed that the variety of sources sampled and the quantity of samples from each source impacted the performance of the model. Most cases were attributed to broilers or cattle for the individual and multi-country models. The proportion of cases that could be attributed with 70% probability to a source decreased when using the down-sampled data set (535 vs. 273 of 2627 cases). The baseline model showed a higher sensitivity compared to the down-sampled model, where samples per source were more evenly distributed. The proportion of cases attributed to non-domestic source was higher but varied depending on the sampling strategy. Both models showed that most cases could be attributed to domestic sources in each country (baseline: 248/273 cases, 91%; down-sampled: 361/535 cases, 67%;). Discussion: The sample sizes per source and the variety of sources included in the model influence the accuracy of the model and consequently the uncertainty of the predicted estimates. The attribution estimates for sources with a high number of samples available tend to be overestimated, whereas the estimates for source with only a few samples tend to be underestimated. Reccomendations for future sampling strategies include to aim for a more balanced sample distribution to improve the overall accuracy and utility of source attribution efforts.eng
dc.description.sponsorshipThis research was funded in part through the Joint Research Project’ Discovering the sources of Salmonella, Campylobacter, VTEC and antimicrobial resistance (DiSCoVer)’ within the One Health European Joint Program (OHEJP), which received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No. 773830. Research at the National Veterinary Research Institute (PIWet), Poland, was also partially supported by the Polish Ministry of Education and Science from the funds for science in the years 2018–2022 allocated for the implementation of a co-financed international project.
dc.identifier.citationFront Microbiol. 2025 Feb 5:16:1519189. doi: 10.3389/fmicb.2025.1519189. eCollection 2025
dc.identifier.doi10.3389/fmicb.2025.1519189
dc.identifier.eissn1664-302X
dc.identifier.pmid39973931
dc.identifier.urihttp://hdl.handle.net/10400.18/10614
dc.language.isoeng
dc.peerreviewedyes
dc.publisherFrontiers Media
dc.relationPromoting One Health in Europe through joint actions on foodborne zoonoses, antimicrobial resistance and emerging microbiological hazards.
dc.relation.hasversionhttps://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2025.1519189/full
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectSource Attribution
dc.subjectFoodborne Disease
dc.subjectCampylobacteriosis
dc.subjectMachine Learning
dc.subjectEuropean Union
dc.subjectInfecções Gastrointestinais
dc.titleSource attribution of human infection: a multi-country model in the European Unioneng
dc.typejournal article
dcterms.referenceshttps://www.frontiersin.org/articles/10.3389/fmicb.2025.1519189/full#supplementary-material
dspace.entity.typePublication
oaire.awardTitlePromoting One Health in Europe through joint actions on foodborne zoonoses, antimicrobial resistance and emerging microbiological hazards.
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/773830/EU
oaire.citation.startPage1519189
oaire.citation.titleFrontiers in Microbiology
oaire.citation.volume16
oaire.fundingStreamH2020
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.nameEuropean Commission
relation.isProjectOfPublication94d118fb-33ce-49fa-b1ed-d5bddf63581d
relation.isProjectOfPublication.latestForDiscovery94d118fb-33ce-49fa-b1ed-d5bddf63581d

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