Using machine learning to attribute the source of antimicrobial resistant campylobacteriosis in the United States

Background The construction of large genome datasets of infectious pathogens in combination with advanced bioinformatics methods has the potential to inform public health risk and targeted intervention strategies. In this study, we use the two most common Campylobacter species in human gastrointestinal infection as model organisms to test the use of machine learning methods for probabilistic assignment of genome sequenced cases of campylobacteriosis in the United States between 2009 and 2019 to potential source reservoirs. These enteric bacteria are ubiquitous in the gut of wild and domestic birds, agricultural mammals and commonly infect humans via consumption of contaminated food. Rising incidence and antimicrobial resistance (AMR) are major concerns and there is an urgent need to quantify the main routes to human infection. Methods As part of routine US national surveillance (2009 through 2019), 8,889 Campylobacter isolate genomes were sequenced from human infections and 15,924 from possible sources. Targeting genetic variation associated with adaptation to the most recent host, we used machine learning and probabilistic models to attribute the source of human infections and estimate the relative importance of different disease reservoirs. Findings Probabilistic attribution identified poultry as the primary infection source of human clinical isolates, responsible for an estimated 72% of cases. Most of the remaining clinical isolates were attributed to cattle (25%), with only a small contribution from wild bird (2%) and pork sources (2%). Fluoroquinolone and aminoglycoside resistant isolates increased overall and among infections attributed to chicken. Interpretation National-scale surveillance and quantification of the relative contribution of infection reservoirs 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 strains.