Assembled C. coli genomes for the publication: Machine learning to attribute the source of Campylobacter infections in the United States: a retrospective analysis of national surveillance data

Methods

As part of routine US national surveillance (2009 through 2019), 8,856 Campylobacter isolate genomes were sequenced from human infections and 16,703 from possible sources. Targeting genetic variation associated with host adaptation, we used machine learning and probabilistic models to attribute the source of human infections and estimate the relative importance of different disease reservoirs. Results Poultry was identified as the primary source of human infection, responsible for an estimated 68% of cases.

Most of the remaining isolates were attributed to cattle (28%), with only a small contribution from wild bird (3%) and pork sources (1%). There was also evidence of an increase in multidrug resistance, particularly fluoroquinolone and aminoglycoside resistance among isolates attributed to chickens. Conclusions National-scale surveillance and quantification of the relative contribution of infection sources can guide policy.

Our study suggests that the greatest reductions in human campylobacteriosis in the US will come from interventions that focus on poultry, which may also reduce the spread of AMR.