A systematic knowledge synthesis on the spatial dimensions of Q fever epidemics.

From 2007 through 2010, the Netherlands experienced the largest Q fever epidemic ever reported. This study integrates the outcomes of a multidisciplinary research programme on spatial airborne transmission of Coxiella burnetii and reflects these outcomes in relation to other scientific Q fever studies worldwide. We have identified lessons learned and remaining knowledge gaps. This synthesis was structured according to the four steps of quantitative microbial risk assessment (QMRA): (a) Rapid source identification was improved by newly developed techniques using mathematical disease modelling; (b) source characterization efforts improved knowledge but did not provide accurate C. burnetii emission patterns; (c) ambient air sampling, dispersion and spatial modelling promoted exposure assessment; and (d) risk characterization was enabled by applying refined dose-response analyses. The results may support proper and timely risk assessment and risk management during future outbreaks, provided that accurate and structured data are available and exchanged readily between responsible actors.

Two Community Clusters of Legionnaires' Disease Directly Linked to a Biologic Wastewater Treatment Plant, the Netherlands.

A biologic wastewater treatment plant was identified as a common source for 2 consecutive Legionnaires' disease clusters in the Netherlands in 2016 and 2017. Sequence typing and transmission modeling indicated direct and long-distance transmission of Legionella, indicating this source type should also be investigated in sporadic Legionnaires' disease cases.

Integrating interdisciplinary methodologies for One Health: goat farm re-implicated as the probable source of an urban Q fever outbreak, the Netherlands, 2009.

BACKGROUND: In spring 2008, a goat farm experiencing Q fever abortions ("Farm A") was identified as the probable source of a human Q fever outbreak in a Dutch town. In 2009, a larger outbreak with 347 cases occurred in the town, despite no clinical Q fever being reported from any local farm. METHODS: Our study aimed to identify the source of the 2009 outbreak by applying a combination of interdisciplinary methods, using data from several sources and sectors, to investigate seventeen farms in the area: namely, descriptive epidemiology of notified cases; collation of veterinary data regarding the seventeen farms; spatial attack rate and relative risk analyses; and GIS mapping of farms and smooth incidence of cases. We conducted further spatio-temporal analyses that integrated temporal data regarding date of onset with spatial data from an atmospheric dispersion model with the most highly suspected source at the centre. RESULTS: Our analyses indicated that Farm A was again the most likely source of infection, with persons living within 1 km of the farm at a 46 times larger risk of being a case compared to those living within 5-10 km. The spatio-temporal analyses demonstrated that about 60 - 65 % of the cases could be explained by aerosol transmission from Farm A assuming emission from week 9; these explained cases lived significantly closer to the farm than the unexplained cases (p = 0.004). A visit to Farm A revealed that there had been no particular changes in management during the spring/summer of 2009, nor any animal health problems around the time of parturition or at any other time during the year. CONCLUSIONS: We conclude that the probable source of the 2009 outbreak was the same farm implicated in 2008, despite animal health indicators being absent. Veterinary and public health professionals should consider farms with past as well as current history of Q fever as potential sources of human outbreaks.

Improved correlation of human Q fever incidence to modelled C. burnetii concentrations by means of an atmospheric dispersion model.

BACKGROUND: Atmospheric dispersion models (ADMs) may help to assess human exposure to airborne pathogens. However, there is as yet limited quantified evidence that modelled concentrations are indeed associated to observed human incidence. METHODS: We correlated human Q fever (caused by the bacterium Coxiella burnetii) incidence data in the Netherlands to modelled concentrations from three spatial exposure models: 1) a NULL model with a uniform concentration distribution, 2) a DISTANCE model with concentrations proportional to the distance between the source and residential addresses of patients, and 3) concentrations modelled by an ADM using three simple emission profiles. We used a generalized linear model to correlate the observed incidences to modelled concentrations and validated it using cross-validation. RESULTS: ADM concentrations generally correlated the best to the incidence data. The DISTANCE model always performed significantly better than the NULL model. ADM concentrations based on wind speeds exceeding threshold values of 0 and 2 m/s performed better than those based on 4 or 6 m/s. This might indicate additional exposure to bacteria originating from a contaminated environment. CONCLUSIONS: By adding meteorological information the correlation between modelled concentration and observed incidence improved, despite using three simple emission profiles. Although additional information is needed - especially regarding emission data - these results provide a basis for the use of ADMs to predict and to visualize the spread of airborne pathogens during livestock, industry and even bio-terroristic related outbreaks or releases to a surrounding human population.

Characteristics of hospitalized acute Q fever patients during a large epidemic, The Netherlands.

BACKGROUND: From 2007 to 2009, The Netherlands experienced a major Q fever epidemic, with higher hospitalization rates than the 2-5% reported in the literature for acute Q fever pneumonia and hepatitis. We describe epidemiological and clinical features of hospitalized acute Q fever patients and compared patients presenting with Q fever pneumonia with patients admitted for other forms of community-acquired pneumonia (CAP). We also examined whether proximity to infected ruminant farms was a risk factor for hospitalization. METHODS: A retrospective cohort study was conducted for all patients diagnosed and hospitalized with acute Q fever between 2007 and 2009 in one general hospital situated in the high incidence area in the south of The Netherlands. Pneumonia severity scores (PSI and CURB-65) of acute Q fever pneumonia patients (defined as infiltrate on a chest x-ray) were compared with data from CAP patients. Hepatitis was defined as a >twofold the reference value for alanine aminotransferase and for bilirubin. RESULTS: Among the 183 hospitalized acute Q fever patients, 86.0% had pneumonia. Elevated liver enzymes (alanine aminotransferase) were found in 32.3% of patients, although hepatitis was not observed in any of them. The most frequent clinical signs upon presentation were fever, cough and dyspnoea. The median duration of admission was five days. Acute Q fever pneumonia patients were younger, had less co-morbidity, and lower PSI and CURB-65 scores than other CAP patients. Anecdotal information from attending physicians suggests that some patients were admitted because of severe subjective dyspnoea, which was not included in the scoring systems. Proximity to an infected ruminant farm was not associated with hospitalization. CONCLUSION: Hospitalized Dutch acute Q fever patients mostly presented with fever and pneumonia. Patients with acute Q fever pneumonia were hospitalized despite low PSI and CURB-65 scores, presumably because subjective dyspnoea was not included in the scoring systems.

A model for the early identification of sources of airborne pathogens in an outdoor environment.

BACKGROUND: Source identification in areas with outbreaks of airborne pathogens is often time-consuming and expensive. We developed a model to identify the most likely location of sources of airborne pathogens. METHODS: As a case study, we retrospectively analyzed three Q fever outbreaks in the Netherlands in 2009, each with suspected exposure from a single large dairy goat farm. Model input consisted only of case residential addresses, day of first clinical symptoms, and human population density data. We defined a spatial grid and fitted an exponentially declining function to the incidence-distance data of each grid point. For any grid point with a fit significant at the 95% confidence level, we calculated a measure of risk. For validation, we used results from abortion notifications, voluntary (2008) and mandatory (2009) bulk tank milk sampling at large (i.e. >50 goats and/or sheep) dairy farms, and non-systematic vaginal swab sampling at large and small dairy and non-dairy goat/sheep farms. In addition, we performed a two-source simulation study. RESULTS: Hotspots--areas most likely to contain the actual source--were identified at early outbreak stages, based on the earliest 2-10% of the case notifications. Distances between the hotspots and suspected goat farms varied from 300-1500 m. In regional likelihood rankings including all large dairy farms, the suspected goat farms consistently ranked first. The two-source simulation study showed that detection of sources is most clear if the distance between the sources is either relatively small or relatively large. CONCLUSIONS: Our model identifies the most likely location of sources in an airborne pathogen outbreak area, even at early stages. It can help to reduce the number of potential sources to be investigated by microbial testing and to allow rapid implementation of interventions to limit the number of human infections and to reduce the risk of source-to-source transmission.