High performance Legionella pneumophila source attribution using genomics-based machine learning classification

Abstract

Identifying the sources of Legionnaires’ disease outbreaks is crucial for effective control. Current genomic methods, while useful, often fall short due to the complex ecology and population structure of Legionella pneumophila , the causative agent. Our study introduces a high-performing machine learning approach for more accurate geographical source attribution of Legionnaires’ disease outbreaks. Developed using cross-validation on environmental L. pneumophila genomes, our models demonstrate excellent predictive sensitivity and specificity. Importantly, this new approach outperforms traditional methods like phylogenomic trees and core genome multi-locus sequence typing, proving more efficient at leveraging genomic variation data to infer outbreak sources. Our machine learning algorithms, harnessing both core and accessory genomic variation, offer significant promise in public health settings. By enabling rapid and precise source identification in Legionnaires’ disease outbreaks, such approaches have the potential to expedite intervention efforts and curtail disease transmission.