A new article published in J. of Infection (September 06, 2024): “Machine learning to attribute the source of Campylobacter infections in the United States: a retrospective analysis of national surveillance data,” describes how integrating pathogen genomic surveillance with bioinformatics can enhance public health responses by identifying risk and guiding interventions. The study focuses on the two predominant Campylobacter species, commonly found in the gut of birds and mammals and often infect humans via contaminated food. Rising incidence and antimicrobial resistance (AMR) are a global concern, and there is an urgent need to quantify the main routes to human infection. 8,856 Campylobacter genomes from human infections and 16,703 from possible sources were sequenced. Using machine learning and probabilistic models, the researchers target genetic variation associated with host adaptation to attribute the source of human infections and estimate the importance of different disease reservoirs. Poultry was identified as the primary source of human infections, responsible for an estimated 68% of cases, followed by cattle (28%), and only a small contribution from wild birds (3%) and pork sources (1%). There was also evidence of increased multidrug resistance, particularly among isolates attributed to chickens. National surveillance and source attribution can guide policy, and our study suggests that interventions targeting poultry will yield the greatest reductions in campylobacteriosis and the spread of AMR in the US. @ https://www.journalofinfection.com/article/S0163-4453(24)00199-3/fulltext
