Journal article

A supervised statistical learning approach for accurateLegionella pneumophilasource attribution during outbreaks

Andrew Buultjens, Kyra Chua, Sarah Baines, Jason Kwong, Wei Gao, Zoe Cutcher, Stuart Adcock, Susan Ballard, Mark Schultz, Takehiro Tomita, Nela Subasinghe, Glen Carter, Sacha Pidot, Lucinda Franklin, Torsten Seemann, Anders Gonçalves Da Silva, Benjamin Howden, Timothy Stinear

Published : 2017

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..

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