scholarly journals A supervised statistical learning approach for accurateLegionella pneumophilasource attribution during outbreaks

2017 ◽  
Author(s):  
Andrew H. Buultjens ◽  
Kyra Y. L. Chua ◽  
Sarah L. Baines ◽  
Jason Kwong ◽  
Wei Gao ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Legionella Pneumophila ◽  
Core Genome ◽  
Cooling Tower ◽  
Sequence Data ◽  
Disease Outbreaks ◽  
Epidemiological Data ◽  
Learning Approach ◽  
Outbreak Investigations ◽  
Legionnaires Disease

AbstractPublic 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 multipleL. pneumophilagenotypes in a single source. These factors can confound cluster identification using standard phylogenomic methods. Here, we show that a statistical learning approach based onL. pneumophilacore genome single nucleotide polymorphism (SNP) comparisons eliminates ambiguity for defining outbreak clusters and accurately predicts exposure sources for clinical cases. We illustrate the performance of our method by genome comparisons of 234L. pneumophilaisolates obtained from patients and cooling towers in Melbourne, Australia between 1994 and 2014. This collection included one of the largest reported Legionnaires’ disease outbreaks, involving 125 cases at an aquarium. Using only sequence data fromL. pneumophilacooling tower isolates and including all core genome variation, we built a multivariate model using discriminant analysis of principal components (DAPC) to find cooling tower-specific genomic signatures, and then used it to predict the origin of clinical isolates. Model assignments were 93% congruent with epidemiological data, including the aquarium Legionnaires’ outbreak and three other unrelated outbreak investigations. We applied the same approach to a recently described investigation of Legionnaires’ disease within a UK hospital and observed model predictive ability of 86%. We have developed a promising means to breachL. pneumophilagenetic diversity extremes and provide objective source attribution data for outbreak investigations.

scholarly journals A Supervised Statistical Learning Approach for Accurate Legionella pneumophila Source Attribution during Outbreaks

2017 ◽  
Vol 83 (21) ◽  
Author(s):  
Andrew H. Buultjens ◽  
Kyra Y. L. Chua ◽  
Sarah L. Baines ◽  
Jason Kwong ◽  
Wei Gao ◽  
...  
Keyword(s):  
Statistical Learning ◽  
Legionella Pneumophila ◽  
Core Genome ◽  
Cooling Tower ◽  
Disease Outbreaks ◽  
Learning Approach ◽  
Source Attribution ◽  
Content Type ◽  
Outbreak Investigations ◽  
Legionnaires Disease

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 clinical cases. We illustrate the performance of our method by genome comparisons of 234 L. pneumophila isolates obtained from patients and cooling towers in Melbourne, Australia, between 1994 and 2014. This collection included one of the largest reported Legionnaires' disease outbreaks, which involved 125 cases at an aquarium. Using only sequence data from L. pneumophila cooling tower isolates and including all core genome variation, we built a multivariate model using discriminant analysis of principal components (DAPC) to find cooling tower-specific genomic signatures and then used it to predict the origin of clinical isolates. Model assignments were 93% congruent with epidemiological data, including the aquarium Legionnaires' disease outbreak and three other unrelated outbreak investigations. We applied the same approach to a recently described investigation of Legionnaires' disease within a UK hospital and observed a model predictive ability of 86%. We have developed a promising means to breach L. pneumophila genetic diversity extremes and provide objective source attribution data for outbreak investigations. IMPORTANCE Microbial outbreak investigations are moving to a paradigm where whole-genome sequencing and phylogenetic trees are used to support epidemiological investigations. It is critical that outbreak source predictions are accurate, particularly for pathogens, like Legionella pneumophila, which can spread widely and rapidly via cooling system aerosols, causing Legionnaires' disease. Here, by studying hundreds of Legionella pneumophila genomes collected over 21 years around a major Australian city, we uncovered limitations with the phylogenetic approach that could lead to a misidentification of outbreak sources. We implement instead a statistical learning technique that eliminates the ambiguity of inferring disease transmission from phylogenies. Our approach takes geolocation information and core genome variation from environmental L. pneumophila isolates to build statistical models that predict with high confidence the environmental source of clinical L. pneumophila during disease outbreaks. We show the versatility of the technique by applying it to unrelated Legionnaires' disease outbreaks in Australia and the UK.

Computational framework for evaluating risk trade-offs in costs associated with Legionnaires’ Disease risk, energy, and scalding risk for hospital hot water systems

2021 ◽  
Author(s):  
Ashley Heida ◽  
Alexis Mraz ◽  
Mark Hamilton ◽  
Mark Weir ◽  
Kerry A Hamilton
Keyword(s):  
Legionella Pneumophila ◽  
Disease Risk ◽  
Disease Outbreaks ◽  
Febrile Illness ◽  
Water Systems ◽  
Hot Water ◽  
Computational Framework ◽  
Trade Offs ◽  
Pontiac Fever ◽  
Legionnaires Disease

Legionella pneumophila are bacteria that when inhaled cause Legionnaires’ Disease (LD) and febrile illness Pontiac Fever. As of 2014, LD is the most frequent cause of waterborne disease outbreaks due...

scholarly journals A cluster of Legionnaires' disease in Belgium linked to a cooling tower, August–September 2016: practical approach and challenges

2019 ◽  
Vol 147 ◽  
Author(s):  
N. Hammami ◽  
V. Laisnez ◽  
I. Wybo ◽  
D. Uvijn ◽  
C. Broucke ◽  
...  
Keyword(s):  
Legionella Pneumophila ◽  
Cooling Tower ◽  
Geographical Information ◽  
Cluster Management ◽  
Respiratory Sample ◽  
Health Authorities ◽  
Public Health Authorities ◽  
Legionnaires Disease ◽  
Environmental Sample Analysis

Abstract A cluster of Legionnaires' disease (LD) with 10 confirmed, three probable and four possible cases occurred in August and September 2016 in Dendermonde, Belgium. The incidence in the district was 7 cases/100 000 population, exceeding the maximum annual incidence in the previous 5 years of 1.5/100 000. Epidemiological, environmental and geographical investigations identified a cooling tower (CT) as the most likely source. The case risk around the tower decreased with increasing distance and was highest within 5 km. Legionella pneumophila serogroup 1, ST48, was identified in a human respiratory sample but could not be matched with the environmental results. Public health authorities imposed measures to control the contamination of the CT and organised follow-up sampling. We identified obstacles encountered during the cluster investigation and formulated recommendations for improved LD cluster management, including faster coordination of teams through the outbreak control team, improved communication about clinical and environmental sample analysis, more detailed documentation of potential exposures obtained through the case questionnaire and earlier use of a geographical information tool to compare potential sources and for hypothesis generation.

scholarly journals Legionnaires' disease and the sick-building syndrome

1989 ◽  
Vol 103 (2) ◽  
pp. 285-292 ◽  
Author(s):  
M. O'Mahony ◽  
A. Lakhani ◽  
A. Stephens ◽  
J. G. Wallace ◽  
E. R. Youngs ◽  
...  
Keyword(s):  
Legionella Pneumophila ◽  
Local Community ◽  
Cooling Tower ◽  
Case Control Study ◽  
Sick Building Syndrome ◽  
Air Conditioning System ◽  
Sick Building ◽  
Air Intake ◽  
Legionnaires Disease ◽  
Control Study

SUMMARYIn October 1985, six cases of legionnaires' disease were associated with a police headquarters building. Four were amongst staff who worked in or visited the communications wing of the headquarters and two cases occurred in the local community. A case-control study implicated the operations room of the communications wing as the main area associated with infection. This wing was air-conditioned and smoke tracer studies showed that drift from the exhaust as well as from the base of the cooling tower entered the main air-intake which serviced the air-conditioning system.Legionella pneumophilaserogroup 1 subgroup pontiac was isolated from water and sludge in the cooling tower pond. Contaminated drift from the top of the cooling tower was probably responsible for the two community cases. An additional discovery was that symptoms suggestive of the sick-building syndrome were associated with working in this wing.

scholarly journals Legionnaires' disease outbreak in Rome, Italy

2005 ◽  
Vol 133 (5) ◽  
pp. 853-859 ◽  
Author(s):  
M. C. ROTA ◽  
G. PONTRELLI ◽  
M. SCATURRO ◽  
A. BELLA ◽  
A. R. BELLOMO ◽  
...  
Keyword(s):  
Legionella Pneumophila ◽  
Cooling Tower ◽  
Case Control Study ◽  
Hospital Admissions ◽  
Case Control ◽  
Phenotypic Analysis ◽  
Single Strain ◽  
Increased Risk ◽  
Legionnaires Disease ◽  
Control Study

Between August and October 2003, 15 cases of Legionnaires' disease were detected in the 9th district of Rome. To identify possible sources of Legionella exposure, a matched case-control study was conducted and environmental samples were collected. Hospital discharge records were also retrospectively analysed for the period July–November 2003, and results were compared with the same period during the previous 3 years. The case-control study revealed a significantly increased risk of disease among those frequenting a specific department store in the district (OR 9·8, 95% CI 2·1–46·0), and Legionella pneumophila was isolated from the store's cooling tower. Genotypic and phenotypic analysis of human and environmental isolates demonstrated that the cluster was caused by a single strain of L. pneumophila serogroup 1, and that the cooling tower of the store was the source of infection. The increased number of hospital admissions for microbiologically undiagnosed pneumonia during the study period may indicate that some legionellosis cases were not identified.

scholarly journals Core Genome Multilocus Sequence Typing: a Standardized Approach for Molecular Typing of Mycoplasma gallisepticum

2017 ◽  
Vol 56 (1) ◽  
Author(s):  
Mostafa Ghanem ◽  
Leyi Wang ◽  
Yan Zhang ◽  
Scott Edwards ◽  
Amanda Lu ◽  
...  
Keyword(s):  
Multilocus Sequence Typing ◽  
Phylogenetic Trees ◽  
Core Genome ◽  
Sequence Data ◽  
Mycoplasma Gallisepticum ◽  
Content Type ◽  
Next Generation Sequencing Technology ◽  
Animal Pathogens ◽  
Conserved Genes ◽  
Outbreak Investigations

ABSTRACT Mycoplasma gallisepticum is the most virulent and economically important Mycoplasma species for poultry worldwide. Currently, M. gallisepticum strain differentiation based on sequence analysis of 5 loci remains insufficient for accurate outbreak investigation. Recently, whole-genome sequences (WGS) of many human and animal pathogens have been successfully used for microbial outbreak investigations. However, the massive sequence data and the diverse properties of different genes within bacterial genomes results in a lack of standard reproducible methods for comparisons among M. gallisepticum whole genomes. Here, we proposed the development of a core genome multilocus sequence typing (cgMLST) scheme for M. gallisepticum strains and field isolates. For development of this scheme, a diverse collection of 37 M. gallisepticum genomes was used to identify cgMLST targets. A total of 425 M. gallisepticum conserved genes (49.85% of M. gallisepticum genome) were selected as core genome targets. A total of 81 M. gallisepticum genomes from 5 countries on 4 continents were typed using M. gallisepticum cgMLST. Analyses of phylogenetic trees generated by cgMLST displayed a high degree of agreement with geographical and temporal information. Moreover, the high discriminatory power of cgMLST allowed differentiation between M. gallisepticum strains of the same outbreak. M. gallisepticum cgMLST represents a standardized, accurate, highly discriminatory, and reproducible method for differentiation among M. gallisepticum isolates. cgMLST provides stable and expandable nomenclature, allowing for comparison and sharing of typing results among laboratories worldwide. cgMLST offers an opportunity to harness the tremendous power of next-generation sequencing technology in applied avian mycoplasma epidemiology at both local and global levels.

scholarly journals Inferring epidemiological links from deep sequencing data: a statistical learning approach for human, animal and plant diseases

2019 ◽  
Vol 374 (1775) ◽  
pp. 20180258 ◽  
Author(s):  
M. Alamil ◽  
J. Hughes ◽  
K. Berthier ◽  
C. Desbiez ◽  
G. Thébaud ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Statistical Learning ◽  
Sequence Data ◽  
Disease Outbreaks ◽  
Plant Diseases ◽  
Training Data ◽  
Contact Tracing ◽  
Sequencing Data ◽  
Theme Issue ◽  
Infectious Disease Outbreaks

Pathogen sequence data have been exploited to infer who infected whom, by using empirical and model-based approaches. Most of these approaches exploit one pathogen sequence per infected host (e.g. individual, household, field). However, modern sequencing techniques can reveal the polymorphic nature of within-host populations of pathogens. Thus, these techniques provide a subsample of the pathogen variants that were present in the host at the sampling time. Such data are expected to give more insight on epidemiological links than a single sequence per host. In general, a mechanistic viewpoint to transmission and micro-evolution has been followed to infer epidemiological links from these data. Here, we investigate an alternative approach grounded on statistical learning. The idea consists of learning the structure of epidemiological links with a pseudo-evolutionary model applied to training data obtained from contact tracing, for example, and using this initial stage to infer links for the whole dataset. Such an approach has the potential to be particularly valuable in the case of a risk of erroneous mechanistic assumptions, it is sufficiently parsimonious to allow the handling of big datasets in the future, and it is versatile enough to be applied to very different contexts from animal, human and plant epidemiology. This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.

scholarly journals Genomic investigation of a suspected outbreak of Legionella pneumophila ST82 reveals undetected heterogeneity by the present gold-standard methods, Denmark, July to November 2014

2017 ◽  
Vol 22 (25) ◽  
Author(s):  
Susanne Schjørring ◽  
Marc Stegger ◽  
Charlotte Kjelsø ◽  
Berit Lilje ◽  
Jette M Bangsborg ◽  
...  
Keyword(s):  
Legionella Pneumophila ◽  
Gold Standard ◽  
Phylogenetic Analyses ◽  
External Source ◽  
Sequence Type ◽  
The Other ◽  
Outbreak Investigations ◽  
Legionnaires Disease ◽  
The Uk ◽  
Suspected Outbreak

Between July and November 2014, 15 community-acquired cases of Legionnaires´ disease (LD), including four with Legionella pneumophila serogroup 1 sequence type (ST) 82, were diagnosed in Northern Zealand, Denmark. An outbreak was suspected. No ST82 isolates were found in environmental samples and no external source was established. Four putative-outbreak ST82 isolates were retrospectively subjected to whole genome sequencing (WGS) followed by phylogenetic analyses with epidemiologically unrelated ST82 sequences. The four putative-outbreak ST82 sequences fell into two clades, the two clades were separated by ca 1,700 single nt polymorphisms (SNP)s when recombination regions were included but only by 12 to 21 SNPs when these were removed. A single putative-outbreak ST82 isolate sequence segregated in the first clade. The other three clustered in the second clade, where all included sequences had < 5 SNP differences between them. Intriguingly, this clade also comprised epidemiologically unrelated isolate sequences from the UK and Denmark dating back as early as 2011. The study confirms that recombination plays a major role in L. pneumophila evolution. On the other hand, strains belonging to the same ST can have only few SNP differences despite being sampled over both large timespans and geographic distances. These are two important factors to consider in outbreak investigations.

scholarly journals Community outbreak of Legionnaires’ disease in Vic-Gurb, Spain in October and November 2005

2007 ◽  
Vol 12 (3) ◽  
pp. 5-6 ◽  
Author(s):  
M R Sala ◽  
C Arias ◽  
J M Oliva ◽  
A Pedrol ◽  
P Roura ◽  
...  
Keyword(s):  
Central Region ◽  
Legionella Pneumophila ◽  
Cooling Tower ◽  
Weather Conditions ◽  
High Humidity ◽  
Cooling Towers ◽  
Large Area ◽  
Environmental Investigation ◽  
Flat Terrain ◽  
Legionnaires Disease

This paper reports the investigation of a community-acquired outbreak of Legionnaires'; disease in the municipalities of Vic and Gurb (Central Region of Catalonia, Spain). There were 55 cases reported in October and November 2005. An epidemiological and environmental investigation was undertaken. Thirty-five case patients (64%) lived in Vic or Gurb, while 36% had visited or worked in Vic or Gurb during the 10 days before onset of symptoms, but no commonly frequented building could be identified. Water probes for culture were obtained from 30 cooling towers. In five cooling towers of two industrial settings in Gurb (plants A and B), Legionella pneumophila (Lp) serogroup 1 was present. Two Lp-1 strains were recovered from cooling towers in plants A and B. The Lp-1 strain from plant A showed a PGFE profile identical with those obtained from three patients. The exposure to Legionella pneumophila apparently occurred in a large area, since 43 of the 55 cases lived, visited or worked within a distance of 1,800 m from plant A, and six cases in a distance between 2,500 and 3,400 m. The inspections of cooling towers in plant A revealed inadequate disinfectant doses of biocide, non-existent maintenance records on weekends and wrong sample points for routine microbial check-ups. Weather conditions in October 2005 template temperature and high humidity (wind conditions are unappreciable) could have been favourable factors in this outbreak together with the flat terrain of Gurb and Vic area, explaining the extensive horizontal airborne dissemination of contaminated aerosols. The outbreak could have been prevented by proper and correct maintenance of the cooling tower at plant A.

scholarly journals The Stafford outbreak of Legionnaires’ disease

1990 ◽  
Vol 104 (3) ◽  
pp. 361-380 ◽  
Author(s):  
M. C. O'Mahony ◽  
R. E. Stanwell-Smith ◽  
H. E. Tillett ◽  
D. Harper ◽  
J. G. P. Hutchison ◽  
...  
Keyword(s):  
Legionella Pneumophila ◽  
Air Conditioning ◽  
Cooling Tower ◽  
Ventilation System ◽  
Water System ◽  
Cooling Water ◽  
District General Hospital ◽  
Control Measures ◽  
Epidemic Strain ◽  
Legionnaires Disease

SUMMARYA large outbreak of Legionnaires’ disease was associated with Stafford District General Hospital. A total of 68 confirmed cases was treated in hospital and 22 of these patients died. A further 35 patients, 14 of whom were treated at home, were suspected cases of Legionnaires’ disease. All these patients had visited the hospital during April 1985. Epidemiological investigations demonstrated that there had been a high risk of acquiring the disease in the out patient department (OPD), but no risk in other parts of the hospital. The epidemic strain ofLegionella pneumophila, serogroup 1, subgroup Pontiac la was isolated from the cooling water system of one of the air conditioning plants. This plant served several departments of the hospital including the OPD. The water in the cooling tower and a chiller unit which cooled the air entering the OPD were contaminated with legionellae. Bacteriological and engineering investigations showed how the chiller unit could have been contaminated and how an aerosol containing legionellae could have been generated in the U–trap below the chiller unit. These results, together with the epidemiological evidence, suggest that the chiller unit was most likely to have been the major source of the outbreak.Nearly one third of hospital staff had legionella antibodies. These staff were likely to have worked in areas of the hospital ventilated by the contaminated air conditioning plant, but not necessarily the OPD. There was evidence that a small proportion of these staff had a mild legionellosis and that these ‘influenza–like’ illnesses had been spread over a 5–month period. A possible explanation of this finding is that small amounts of aerosol from cooling tower sources could have entered the air–intake and been distributed throughout the areas of the hospital served by this ventilation system. Legionellae, subsequently found to be of the epidemic strain, had been found in the cooling tower pond in November 1984 and thus it is possible that staff were exposed to low doses of contaminated aerosol over several months.Control measures are described, but it was later apparent that the outbreak had ended before these interventions were introduced. The investigations revealed faults in the design of the ventilation system.

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