A supervised statistical learning approach for accurateLegionella pneumophilasource attribution during outbreaks

Abstract

Public health agencies are increasingly relying on genomics during Legionnaires’ disease investigations. However, the causative bacterium ( Legionella pneumophila ) has an unusual population structure with extreme temporal and spatial genome sequence conservation. Furthermore, Legionnaires’ disease outbreaks can be caused by multiple L. pneumophila genotypes in a single source. These factors can confound cluster identification using standard phylogenomic methods. Here, we show that a statistical learning approach based on L. pneumophila core genome single nucleotide polymorphism (SNP) comparisons eliminates ambiguity for defining outbreak clusters and accurately predicts exposure sources for..