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bioRxiv · 10.1101/2022.09.26.509626

Can genomics and meteorology predict outbreaks of legionellosis in urban settings?

Timms, V. J.·Sim, E.·Pey, K.·Sintchenko, V.
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Abstract

Legionella pneumophila is ubiquitous and sporadically infects humans causing Legionnaires disease (LD). Globally, reported cases of LD has risen four-fold from 2000-2014. In 2016, Sydney, Australia was the epicentre of an outbreak caused by L. pneumophila serogroup 1 (Lpsg1). Whole genome sequencing was instrumental in identifying the causal clone which was found in multiple locations across the city. This study examined the epidemiology of Lpsg1 in an urban environment, assessed typing schemes to classify resident clones and investigated the association between local climate variables and LD outbreaks. Of 223 local Lpsg1 isolates, we identified dominant clones with one clone isolated from patients in high frequency during outbreak investigations. The cgMLST scheme was the most reliable in identifying this Lpsg1 clone. While an increase in humidity and rainfall was found to coincide with a rise in LD cases, the incidence of the major L. pneumophila outbreak clone did not link to weather phenomena. These findings demonstrated the role of high resolution typing and weather context assessment in determining source attribution for LD outbreaks in urban settings, particularly when clinical isolates remain scarce. ImportanceWe investigated the genomic and meteorological influences of infections caused by Legionella pneumophila in Sydney, Australia. Our study contributes to a knowledge gap of factors that drive outbreaks of legionellosis compared to sporadic infections in urban settings. In such cases, clinical isolates can be rare and other data is then relied upon to inform decision making around control measures. We found that cgMLST typing offered a robust and scalable approach for high-resolution investigation of Lpsg1 outbreaks. The genomic landscape of Lpsg1 in Sydney was dominated by a single clone which was responsible for multiple clusters of community cases over four decades. While legionellosis incidence peaked in Autumn, this was not linked to the dominant outbreak clone. The synthesis of meteorological data with Lpsg1 genomics can be a part of the risk assessment for legionellosis in urban settings and is relevant for other densely populated areas around the world.

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Timms, V. J., Sim, E., Pey, K., Sintchenko, V.. 2022-09-28. Can genomics and meteorology predict outbreaks of legionellosis in urban settings?. https://doi.org/10.1101/2022.09.26.509626

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