ArticleFrontiers in microbiology2025
Source attribution of human
Article in Frontiers in microbiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Multisectoral Emergence of Multidrug-Resistant Campylobacter coli Sequence Type 10042 Lineage, Europe, 2018-2025.Emerging infectious diseases · 2026Article
- Sources of Human Campylobacteriosis Cases in Estonia and the Genomic Associations with Broiler Chicken Meat Isolates.Pathogens (Basel, Switzerland) · 2026Article
Corrections and comments
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Authors and funding
29 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Infections caused by Methods: We constructed a machine-learning model using 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.
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Registered trials
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