Machine learning to attribute the source of Campylobacter infections in the United States: A retrospective analysis of national surveillance data.

ABSTRACT:

Objectives

Integrating pathogen genomic surveillance with bioinformatics can enhance public health responses by identifying risk and guiding interventions. This study focusses on the two predominant Campylobacter species, which are 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.

Methods

During routine US national surveillance (2009-2019), 8856 Campylobacter genomes from human infections and 16,703 from possible sources were sequenced. Using machine learning and probabilistic models, we target genetic variation associated with host adaptation to attribute the source of human infectio

SUBMITTER: Pascoe B 

PROVIDER: S-EPMC7617841 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature