Attributing Campylobacter infections in the US using machine learning: A retrospective analysis
A new study published in the Journal of Infection investigates the sources of Campylobacter infections in the United States using genomic data and machine learning techniques. It analyzes 8,856 Campylobacter genomes from human infections and 16,703 from potential sources collected during national surveillance from 2009 to 2019.
The main conclusions of the study are as follows:
-Poultry was identified as the primary source of human Campylobacter infections, accounting for approximately 68% of cases. Cattle contributed to 28% of infections, while wild birds and pork were responsible for only 3% and 1%, respectively.
-The study found a concerning rise in multidrug-resistant (MDR) Campylobacter isolates, particularly those linked to poultry sources, highlighting a significant public health risk.
-There has been a notable increase in the attribution of Campylobacter infections to poultry over the years, indicating a growing need for targeted interventions in this sector.
-The findings suggest that interventions aimed at reducing Campylobacter contamination in poultry could lead to significant decreases in campylobacteriosis cases and the spread of antimicrobial resistance.
-The study emphasizes the importance of routine genomic surveillance and machine learning in tracking infection sources, which can inform public health policies and improve food safety measures.
