Comparison of Source Attribution Methodologies for Human Campylobacteriosis

Maja Lykke Brinch, Tine Hald, Lynda Wainaina, Alessandra Merlotti, Daniel Remondini, Clementine Henri, Patrick Murigu Kamau Njage*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Campylobacter spp. are the most common cause of bacterial gastrointestinal infection in humans both in Denmark and worldwide. Studies have found microbial subtyping to be a powerful tool for source attribution, but comparisons of different methodologies are limited. In this study, we compare three source attribution approaches (Machine Learning, Network Analysis, and Bayesian modeling) using three types of whole genome sequences (WGS) data inputs (cgMLST, 5-Mers and 7-Mers). We predicted and compared the sources of human campylobacteriosis cases in Denmark. Using 7mer as an input feature provided the best model performance. The network analysis algorithm had a CSC value of 78.99% and an F1-score value of 67%, while the machine-learning algorithm showed the highest accuracy (98%). The models attributed between 965 and all of the 1224 human cases to a source (network applying 5mer and machine learning applying 7mer, respectively). Chicken from Denmark was the primary source of human campylobacteriosis with an average percentage probability of attribution of 45.8% to 65.4%, representing Bayesian with 7mer and machine learning with cgMLST, respectively. Our results indicate that the different source attribution methodologies based on WGS have great potential for the surveillance and source tracking of Campylobacter. The results of such models may support decision makers to prioritize and target interventions.
Original languageEnglish
Article number786
JournalPathogens
Volume12
Issue number6
Number of pages13
ISSN2076-0817
DOIs
Publication statusPublished - 2023

Bibliographical note

This research was funded in part through the Joint Research Project ‘Discovering the sources of Salmonella, Campylobacter, VTECand 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.

Keywords

  • Source attribution
  • Campylobacter
  • Campylobacteriosis
  • Network analysis
  • Whole-genome sequencing
  • Coherence source clustering
  • Machine learning
  • Bayesian modelling

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