Barrow-in-Furness: a large community legionellosis outbreak in the UK

Opportunistic Premise Plumbing Pathogens. A Potential Health Risk in Water Mist Systems Used as a Cooling Intervention

Water mist systems (WMS) are used for evaporative cooling in public areas. The health risks associated with their colonization by opportunistic premise plumbing pathogens (OPPPs) is not well understood. To advance the understanding of the potential health risk of OPPPs in WMS, biofilm, water and bioaerosol samples (n = 90) from ten (10) WMS in Australia were collected and analyzed by culture and polymerase chain reaction (PCR) methods to detect the occurrence of five representative OPPPs: Legionella pneumophila, Pseudomonas aeruginosa, Mycobacterium avium, Naegleria fowleri and Acanthamoeba. P. aeruginosa (44%, n = 90) occurred more frequently in samples, followed by L. pneumophila serogroup (Sg) 2–14 (18%, n = 90) and L. pneumophila Sg 1 (6%, n = 90). A negative correlation between OPPP occurrence and residual free chlorine was observed except with Acanthamoeba, rs (30) = 0.067, p > 0.05. All detected OPPPs were positively correlated with total dissolved solids (TDS) except with Acanthamoeba. Biofilms contained higher concentrations of L. pneumophila Sg 2–14 (1000–3000 CFU/mL) than water samples (0–100 CFU/mL). This study suggests that WMS can be colonized by OPPPs and are a potential health risk if OPPP contaminated aerosols get released into ambient atmospheres.

Dispersion of Legionella bacteria in atmosphere: A practical source location estimation method

Legionnaires’ disease, a form of pneumonia which can be fatal, is transmitted via the inhalation of water droplets containing Legionella bacteria. These droplets can be dispersed in the atmosphere several kilometers from their source. The most common such sources are contaminated water within cooling towers and other air-conditioning systems but other sources such as ornamental fountains and spa pools have also caused outbreaks of the disease in the past. There is an obvious need to locate and eliminate any such sources as quickly as possible. Here a maximum likelihood model estimating the source of an outbreak from case location data has been developed and implemented. Unlike previous models, the average dose exposure sub-model is formulated using a atmospheric dispersion model. How the uncertainty in inferred parameters can be estimated is discussed. The model is applied to the 2012 Edinburgh Legionnaires’ disease outbreak.

Legionnaires' Disease Outbreaks and Cooling Towers, New York City, New York, USA

Surveillance will determine whether a new law regulating cooling towers reduces the incidence of Legionnaires’ disease.

The incidence of Legionnaires’ disease in the United States has been increasing since 2000. Outbreaks and clusters are associated with decorative, recreational, domestic, and industrial water systems, with the largest outbreaks being caused by cooling towers. Since 2006, 6 community-associated Legionnaires’ disease outbreaks have occurred in New York City, resulting in 213 cases and 18 deaths. Three outbreaks occurred in 2015, including the largest on record (138 cases). Three outbreaks were linked to cooling towers by molecular comparison of human and environmental Legionella isolates, and the sources for the other 3 outbreaks were undetermined. The evolution of investigation methods and lessons learned from these outbreaks prompted enactment of a new comprehensive law governing the operation and maintenance of New York City cooling towers. Ongoing surveillance and program evaluation will determine if enforcement of the new cooling tower law reduces Legionnaires’ disease incidence in New York City.

Legionella longbeachae detected in an industrial cooling tower linked to a legionellosis outbreak, New Zealand, 2015; possible waterborne transmission?

A legionellosis outbreak at an industrial site was investigated to identify and control the source. Cases were identified from disease notifications, workplace illness records, and from clinicians. Cases were interviewed for symptoms and risk factors and tested for legionellosis. Implicated environmental sources were sampled and tested for legionella. We identified six cases with Legionnaires' disease and seven with Pontiac fever; all had been exposed to aerosols from the cooling towers on the site. Nine cases had evidence of infection with either Legionella pneumophila serogroup (sg) 1 or Legionella longbeachae sg1; these organisms were also isolated from the cooling towers. There was 100% DNA sequence homology between cooling tower and clinical isolates of L. pneumophila sg1 using sequence-based typing analysis; no clinical L. longbeachae isolates were available to compare with environmental isolates. Routine monitoring of the towers prior to the outbreak failed to detect any legionella. Data from this outbreak indicate that L. pneumophila sg1 transmission occurred from the cooling towers; in addition, L. longbeachae transmission was suggested but remains unproven. L. longbeachae detection in cooling towers has not been previously reported in association with legionellosis outbreaks. Waterborne transmission should not be discounted in investigations for the source of L. longbeachae infection.

Rapid investigation of cases and clusters of Legionnaires' disease in England and Wales using direct molecular typing

Legionella pneumophila is the leading cause of Legionnaires' disease, a severe pneumonia that can occur as sporadic cases or point-source outbreaks affecting multiple patients. The infection is acquired by inhalation of aerosols from contaminated water systems. In order to identify the probable source and prevent further cases, clinical and environmental isolates are compared using phenotypic and genotypic methods. Typically up to 10 days are required to isolate L. pneumophila prior to the application of standard typing protocols. A rapid protocol using a real-time PCR specific for L. pneumophila and serogroup 1, combined with nested direct molecular typing, was adopted by Public Health England in 2012 to reduce reporting time for preliminary typing results. This rapid protocol was first used to investigate an outbreak that occurred in July/August 2012 and due to the positive feedback from that investigation, it was subsequently applied to other incidents in England and Wales where faster typing results would have aided incident investigation. We present here results from seven incidents that occurred between July 2012 and June 2015 where the use of this rapid approach provided preliminary characterization of the infecting strain in an average 1.58 days (SD 1.01) after sample receipt in contrast to 9.53 days (SD 3.73) when standard protocols were applied.

A review of back-calculation techniques and their potential to inform mitigation strategies with application to non-transmissible acute infectious diseases

Back-calculation is a process whereby generally unobservable features of an event leading to a disease outbreak can be inferred either in real-time or shortly after the end of the outbreak. These features might include the time when persons were exposed and the source of the outbreak. Such inferences are important as they can help to guide the targeting of mitigation strategies and to evaluate the potential effectiveness of such strategies. This article reviews the process of back-calculation with a particular emphasis on more recent applications concerning deliberate and naturally occurring aerosolized releases. The techniques can be broadly split into two themes: the simpler temporal models and the more sophisticated spatio-temporal models. The former require input data in the form of cases' symptom onset times, whereas the latter require additional spatial information such as the cases' home and work locations. A key aspect in the back-calculation process is the incubation period distribution, which forms the initial topic for consideration. Links between atmospheric dispersion modelling, within-host dynamics and back-calculation are outlined in detail. An example of how back-calculation can inform mitigation strategies completes the review by providing improved estimates of the duration of antibiotic prophylaxis that would be required in the response to an inhalational anthrax outbreak.

Whole-Genome Mapping as a Novel High-Resolution Typing Tool for Legionella pneumophila

Legionella is the causative agent for Legionnaires' disease (LD) and is responsible for several large outbreaks in the world. More than 90% of LD cases are caused by Legionella pneumophila, and studies on the origin and transmission routes of this pathogen rely on adequate molecular characterization of isolates. Current typing of L. pneumophila mainly depends on sequence-based typing (SBT). However, studies have shown that in some outbreak situations, SBT does not have sufficient discriminatory power to distinguish between related and nonrelated L. pneumophila isolates. In this study, we used a novel high-resolution typing technique, called whole-genome mapping (WGM), to differentiate between epidemiologically related and nonrelated L. pneumophila isolates. Assessment of the method by various validation experiments showed highly reproducible results, and WGM was able to confirm two well-documented Dutch L. pneumophila outbreaks. Comparison of whole-genome maps of the two outbreaks together with WGMs of epidemiologically nonrelated L. pneumophila isolates showed major differences between the maps, and WGM yielded a higher discriminatory power than SBT. In conclusion, WGM can be a valuable alternative to perform outbreak investigations of L. pneumophila in real time since the turnaround time from culture to comparison of the L. pneumophila maps is less than 24 h.

Strategies for controlling non-transmissible infection outbreaks using a large human movement data set

Prediction and control of the spread of infectious disease in human populations benefits greatly from our growing capacity to quantify human movement behavior. Here we develop a mathematical model for non-transmissible infections contracted from a localized environmental source, informed by a detailed description of movement patterns of the population of Great Britain. The model is applied to outbreaks of Legionnaires' disease, a potentially life-threatening form of pneumonia caused by the bacteria Legionella pneumophilia. We use case-report data from three recent outbreaks that have occurred in Great Britain where the source has already been identified by public health agencies. We first demonstrate that the amount of individual-level heterogeneity incorporated in the movement data greatly influences our ability to predict the source location. The most accurate predictions were obtained using reported travel histories to describe movements of infected individuals, but using detailed simulation models to estimate movement patterns offers an effective fast alternative. Secondly, once the source is identified, we show that our model can be used to accurately determine the population likely to have been exposed to the pathogen, and hence predict the residential locations of infected individuals. The results give rise to an effective control strategy that can be implemented rapidly in response to an outbreak.

Public health strategies for infectious disease control can benefit greatly from our growing capacity to predict human movement behaviour. This is facilitated by modern methods of electronic data generation and storage that allow us to track detailed human movement patterns. Here we develop a mathematical model of the dynamics of non-transmissible infections that is informed by a new data set describing detailed movements of the population of Great Britain. We apply the model to three outbreaks of Legionnaires' disease. We demonstrate how the method can assist during the crucial early stages of an outbreak by providing predictions of the infection source location and individuals with a high exposure risk.