Smooth incidence maps give valuable insight into Q fever outbreaks in The Netherlands

Molecular and serological prevalence of Coxiella burnetii in bovine dairy herds in southern Chile: A PCR and ELISA-based assessment of bulk tank milk samples

Coxiella burnetii (C. burnetii) is a highly resilient zoonotic bacterium responsible for Q fever, a disease which occurs worldwide, with the exception of New Zealand. However, in Chile, the prevalence and impact of C. burnetii in cattle herds remain poorly understood due to limited research. This study aimed to assess the presence of C. burnetii in dairy cattle herds in southern Chile, using two diagnostic methods on bulk tank milk samples. The results of the study revealed a high prevalence of C. burnetii infection in the analyzed herds. Of the 271 milk tank samples tested, 76% (208/271, CI: 71.1-81.5) tested positive using ELISA, while 73% (200/271, CI: 68.0-78.8) tested positive using qPCR. These findings indicate a significant presence of C. burnetii in the cattle herds studied. Despite the high prevalence observed, no new Q fever outbreaks have been reported in the study area. This discrepancy highlights the need for further research to better understand the transmission dynamics, environmental factors, and livestock management practices associated with C. burnetii infection. These studies will contribute to the development of effective prevention and control strategies and promote public health regarding Q fever.

The First Confirmed Pediatric Chronic Osteomyelitis due to Coxiella Burnetii in Oman

We describe here the first confirmed case in Oman of chronic osteomyelitis due to Coxiella burnetii, in a previously healthy four-year-old Omani girl. After laboratory confirmation of C. burnetii infection using molecular and qualitative and quantitative serological assays, the case was successfully managed with a combination of oral ciprofloxacin and cotrimoxazole and thereafter followed up for a long period without remission.

Monitoring and Surveillance of Small Ruminant Health in The Netherlands

In contemporary society and modern livestock farming, a monitoring and surveillance system for animal health has become indispensable. In addition to obligations arising from European regulations regarding monitoring and surveillance of animal diseases, The Netherlands developed a voluntary system for the monitoring and surveillance of small ruminant health. This system aims for (1) early detection of outbreaks of designated animal diseases, (2) early detection of yet unknown disease conditions, and (3) insight into trends and developments. To meet these objectives, a system is in place based on four main surveillance components, namely a consultancy helpdesk, diagnostic services, multiple networks, and an annual data analysis. This paper describes the current system and its ongoing development and gives an impression of nearly twenty years of performance by providing a general overview of key findings and three elaborated examples of notable disease outbreaks. Results indicate that the current system has added value to the detection of various (re)emerging and new diseases. Nevertheless, animal health monitoring and surveillance require a flexible approach that is able to keep pace with changes and developments within the industry. Therefore, monitoring and surveillance systems should be continuously adapted and improved using new techniques and insights.

Modeling latent spatio-temporal disease incidence using penalized composite link models

Epidemiological data are frequently recorded at coarse spatio-temporal resolutions to protect confidential information or to summarize it in a compact manner. However, the detailed patterns followed by the source data, which may be of interest to researchers and public health officials, are overlooked. We propose to use the penalized composite link model (Eilers PCH (2007)), combined with spatio-temporal P-splines methodology (Lee D.-J., Durban M (2011)) to estimate the underlying trend within data that have been aggregated not only in space, but also in time. Model estimation is carried out within a generalized linear mixed model framework, and sophisticated algorithms are used to speed up computations that otherwise would be unfeasible. The model is then used to analyze data obtained during the largest outbreak of Q-fever in the Netherlands.

Dairy Sheep Played a Minor Role in the 2005-2010 Human Q Fever Outbreak in The Netherlands Compared to Dairy Goats

Q fever is an almost ubiquitous zoonosis caused by Coxiella burnetii. This organism infects several animal species, as well as humans, and domestic ruminants like cattle, sheep and goats are an important animal reservoir of C. burnetii. In 2007, a sudden rise in notified human Q fever cases occurred in The Netherlands, and by the end of 2009, more than 3500 human Q fever patients had been notified. Dairy sheep and dairy goats were suspected to play a causal role in this human Q fever outbreak, and several measures were taken, aiming at a reduction of C. burnetii shedding by infected small ruminants, in order to reduce environmental contamination and thus human exposure. One of the first measures was compulsory notification of more than five percent abortion within thirty days for dairy sheep and dairy goat farms, starting 12 June 2008. After notification, an official farm inspection took place, and laboratory investigations were performed aiming at ruling out or demonstrating a causal role of C. burnetii. These measures were effective, and the number of human Q fever cases decreased; levels are currently the same as they were prior to 2007. The effect of these measures was monitored using a bulk tank milk (BTM) PCR and an antibody ELISA. The percentage PCR positive dairy herds and flocks decreased over time, and dairy sheep flocks tested PCR positive significantly less often and became PCR negative earlier compared to dairy goat herds. Although there was no difference in the percentage of dairy goat and dairy sheep farms with a C. burnetii abortion outbreak, the total number of shedding dairy sheep was much lower than the number of shedding dairy goats. Combined with the fact that Q fever patients lived mainly in the proximity of infected dairy goat farms and that no Q fever patients could be linked directly to dairy sheep farms, although this may have happened in individual cases, we conclude that dairy sheep did not play a major role in the Dutch Q fever outbreak. BTM monitoring using both a PCR and an ELISA is essential to determine a potential C. burnetii risk, not only for The Netherlands but for other countries with small ruminant dairy industries.

Evidence of exposure to C. burnetii among slaughterhouse workers in western Kenya

Q fever, caused by C. burnetii, has been reported in slaughterhouse workers worldwide. The most reported risk factor for seropositivity is the workers' role in the slaughterhouse. This study examined the seroprevalence and risk factors for antibodies to C. burnetii in slaughterhouse workers in western Kenya to fill a data gap relating to this emerging disease in East Africa.

Individuals were recruited from all consenting slaughterhouses in the study area between February and November 2012. Information was collected from participating workers regarding demographic data, animals slaughtered and role in the slaughterhouse. Sera samples were screened for antibodies to C. burnetii using a commercial ELISA and risk factors associated with seropositivity were identified using multi-level logistic regression analysis.

Slaughterhouse workers (n = 566) were recruited from 84 ruminant slaughterhouses in western Kenya. The seroprevalence of antibodies to C. burnetii was 37.1% (95% Confidence Interval (CI) 33.2–41.2%). The risk factors identified for C. burnetii seropositivity included: male workers compared to female workers, odds ratio (OR) 5.40 (95% CI 1.38–21.22); slaughtering cattle and small ruminants compared to those who only slaughtered cattle, OR 1.52 (95% CI 1.06–2.19). In addition, specific roles in the slaughterhouse were associated with increased odds of being seropositive, including cleaning the slaughterhouse, OR 3.98 (95% CI 1.39–11.43); cleaning the intestines, OR 3.24 (95% CI 1.36–7.73); and flaying the carcass OR 2.63 (95% CI 1.46–4.75) compared to being the slaughterman or foreman.

We identified that slaughterhouse workers have a higher seroprevalence of antibodies to C. burnetii compared to published values in the general population from the same area. Slaughterhouse workers therefore represent an occupational risk group in this East African setting. Workers with increased contact with the viscera and fluids are at higher risk for exposure to C. burnetii. Education of workers may reduce transmission, but an alternative approach may be to consider the benefits of vaccination in high-risk groups.

Comparison of Coxiella burnetii Excretion between Sheep and Goats Naturally Infected with One Cattle-Associated Genotype

The main reservoir of Coxiella (C.) burnetii are ruminants. They shed the pathogen through birth products, vaginal mucus, faeces and milk. A direct comparison of C. burnetii excretions between naturally infected sheep and goats was performed on the same farm to investigate species-specific differences. The animals were vaccinated with an inactivated C. burnetii phase I vaccine at the beginning of the study period for public health reasons. Vaginal and rectal swabs along with milk specimens were taken monthly during the lambing period and once again at the next lambing season. To estimate the environmental contamination of the animals' housings, nasal swabs from every animal were taken simultaneously. Moreover, dust samples from the windowsills and straw beddings were collected. All samples were examined by qPCR targeting the IS1111 gene and the MLVA/VNTR typing method was performed. Whole genome sequencing was applied to determine the number of IS1111 copies followed by a calculation of C. burnetii genome equivalents of each sample. The cattle-associated genotype C7 was detected containing 29 IS1111 copies. Overall, goats seem to shed more C. burnetii through vaginal mucus and in particular shed more and for longer via the rectal route than sheep. This is supported by the larger quantities of C. burnetii DNA detected in caprine nasal swabs and environmental samples compared to the ovine ones. Transmission of C. burnetii from cattle to small ruminants must also be considered.

Relationship between Coxiella burnetii (Q fever) antibody serology and time spent outdoors

BACKGROUND/AIM

From 2007 through 2010, the Netherlands experienced the largest recorded Q fever outbreak to date. People living closer to Coxiella burnetii infected goat farms were at increased risk for acute Q fever. Time spent outdoors near infected farms may have contributed to exposure to C. burnetii. The aim of this study was to retrospectively evaluate whether hours/week spent outdoors, in the vicinity of previously C. burnetii infected goat farms, was associated with presence of antibodies against C. burnetii in residents of a rural area in the Netherlands.

METHODS

Between 2014-2015, we collected C. burnetii antibody serology and self-reported data about habitual hours/week spent outdoors near the home from 2494 adults. From a subgroup we collected 941 GPS tracks, enabling analyses of active mobility in the outbreak region. Participants were categorised as exposed if they spent time within specified distances (500m, 1000m, 2000m, or 4000m) of C. burnetii infected goat farms. We evaluated whether time spent near these farms was associated with positive C. burnetii serology using spline analyses and logistic regression.

RESULTS

People that spent more hours/week outdoors near infected farms had a significantly increased risk for positive C. burnetii serology (time spent within 2000m of a C. burnetii abortion-wave positive farm, OR 3.6 (1.2-10.6)), compared to people spending less hours/week outdoors.

CONCLUSIONS

Outdoor exposure contributed to the risk of becoming C. burnetii serology positive. These associations were stronger if people spent more time near C. burnetii infected farms. Outdoor exposure should, if feasible, be included in outbreak investigations.

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.

Airborne geographical dispersal of Q fever from livestock holdings to human communities: a systematic review and critical appraisal of evidence

BACKGROUND

Q fever is a zoonotic disease caused by Coxiella burnetii. This bacterium survives harsh conditions and attaches to dust, suggesting environmental dispersal is a risk factor for outbreaks. Spatial epidemiology studies collating evidence on Q fever geographical contamination gradients are needed, as human cases without occupational exposure are increasing worldwide.

METHODS

We used a systematic literature search to assess the role of distance from ruminant holdings as a risk factor for human Q fever outbreaks. We also collated evidence for other putative drivers of C. burnetii geographical dispersal.

RESULTS

In all documented outbreaks, infective sheep or goats, not cattle, was the likely source. Evidence suggests a prominent role of airborne dispersal; Coxiella burnetii travels up to 18 km on gale force winds. In rural areas, highest infection risk occurs within 5 km of sources. Urban outbreaks generally occur over smaller distances, though evidence on attack rate gradients is limited. Wind speed / direction, spreading of animal products, and stocking density may all contribute to C. burnetii environmental gradients.

CONCLUSIONS

Q fever environmental gradients depend on urbanization level, ruminant species, stocking density and wind speed. While more research is needed, evidence suggests that residential exclusion zones around holdings may be inadequate to contain this zoonotic disease, and should be species-specific.

Dot map cartograms for detection of infectious disease outbreaks: an application to Q fever, the Netherlands and pertussis, Germany

Geographical mapping of infectious diseases is an important tool for detecting and characterising outbreaks. Two common mapping methods, dot maps and incidence maps, have important shortcomings. The former does not represent population density and can compromise case privacy, and the latter relies on pre-defined administrative boundaries. We propose a method that overcomes these limitations: dot map cartograms. These create a point pattern of cases while reshaping spatial units, such that spatial area becomes proportional to population size. We compared these dot map cartograms with standard dot maps and incidence maps on four criteria, using two example datasets. Dot map cartograms were able to illustrate both incidence and absolute numbers of cases (criterion 1): they revealed potential source locations (Q fever, the Netherlands) and clusters with high incidence (pertussis, Germany). Unlike incidence maps, they were insensitive to choices regarding spatial scale (criterion 2). Dot map cartograms ensured the privacy of cases (criterion 3) by spatial distortion; however, this occurred at the expense of recognition of locations (criterion 4). We demonstrate that dot map cartograms are a valuable method for detection and visualisation of infectious disease outbreaks, which facilitates informed and appropriate actions by public health professionals, to investigate and control outbreaks.

Use of a Dose-Response Model to Study Temporal Trends in Spatial Exposure to Coxiella burnetii: Analysis of a Multiyear Outbreak of Q Fever

The Netherlands underwent a large Q fever outbreak between 2007 and 2009. In this paper, we study spatial and temporal Coxiella burnetii exposure trends during this large outbreak as well as validate outcomes against other published studies and provide evidence to support hypotheses on the causes of the outbreak. To achieve this, we develop a framework using a dose-response model to translate acute Q fever case incidence into exposure estimates. More specifically, we incorporate a geostatistical model that accounts for spatial and temporal correlation of exposure estimates from a human Q fever dose-response model to quantify exposure trends during the outbreak. The 2051 cases, with the corresponding age, gender and residential addresses, reside in the region with the highest attack rates during the outbreak in the Netherlands between 2006 and 2009. We conclude that the multiyear outbreak in the Netherlands is caused by sustained release of infectious bacteria from the same sources, which suggests that earlier implementation of interventions may have prevented many of the cases. The model predicts the risk of infection and acute symptomatic Q fever from multiple exposure sources during a multiple-year outbreak providing a robust, evidence-based methodology to support decision-making and intervention design.

Spatial methods for infectious disease outbreak investigations: systematic literature review

Investigations of infectious disease outbreaks are conventionally framed in terms of person, time and place. Although geographic information systems have increased the range of tools available, spatial analyses are used relatively infrequently. We conducted a systematic review of published reports of outbreak investigations worldwide to estimate the prevalence of spatial methods, describe the techniques applied and explore their utility. We identified 80 reports using spatial methods published between 1979 and 2013, ca 0.4% of the total number of published outbreaks. Environmental or waterborne infections were the most commonly investigated, and most reports were from the United Kingdom. A range of techniques were used, including simple dot maps, cluster analyses and modelling approaches. Spatial tools were usefully applied throughout investigations, from initial confirmation of the outbreak to describing and analysing cases and communicating findings. They provided valuable insights that led to public health actions, but there is scope for much wider implementation and development of new methods.

Estimation of acute and chronic Q fever incidence in children during a three-year outbreak in the Netherlands and a comparison with international literature

Background

In the Dutch 2007–2009 Q fever outbreak Coxiella burnetii was transmitted aerogenically from dairy goat farms to those living in the surrounding areas. Relatively few children were reported. The true number of pediatric infections is unknown. In this study, we estimate the expected number of acute and chronic childhood infections.

Methods

As Coxiella was transmitted aerogenic to those living near infected dairy goat farms, we could use adult seroprevalence data to estimate infection risk for inhabitants, children and adults alike. Using Statistics Netherlands data we estimated the number of children at (high) risk for developing chronic Q fever. Literature was reviewed for childhood (0–15 years) Q fever reports and disease rates. We compared this with Dutch reported and our estimated data for 2007–2009.

Results

In The Netherlands epidemic, 44 children were reported (1.2 % of total notifications). The childhood incidence was 0.15 compared to 2.6 per 10,000 inhabitants for adults. No complications were reported. Based on the expected similarity in childhood and adult exposure we assume that 9.8 % of children in the high-risk area had Q fever infection, resulting in 1562 acute infections during the Q fever epidemic interval. Based on the prevalence of congenital heart disease, at least 13 children are at high risk for developing chronic Q fever. In medical literature, 42 case reports described 140 childhood Q fever cases with a serious outcome (four deaths). In chronic Q fever, cardiac infections were predominant. Four outbreaks were reported involving children, describing 11 childhood cases. 36 National and/or regional studies reported seroprevalences varying between 0 and 70 %.

Conclusion

In the 3-year Dutch epidemic, few childhood cases were reported, with pulmonary symptoms leading, and none with a serious presentation. With an estimated 13 high-risk children for chronic infection in the high exposure area, and probably forty in the whole country, we may expect several chronic Q fever complications in the coming years in paediatric practice.

Electronic supplementary material

The online version of this article (doi:10.1186/s13104-015-1389-0) contains supplementary material, which is available to authorized users.

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.

Evaluation of Patients with Community-Acquired Pneumonia Caused by Zoonotic Pathogens in an Area with a High Density of Animal Farms

Intensive animal farming could potentially lead to outbreaks of infectious diseases. Clinicians are at the forefront of detecting unusual diseases, but the lack of specificity of zoonotic disease symptoms makes this a challenging task. We evaluated patients with community-acquired pneumonia (CAP) with known and unknown aetiology in an area with a high livestock density and a potential association with animal farms in the proximity. Between 2008 and 2009, a period coinciding with a large Q fever outbreak in the Netherlands, patients with CAP were tested for the presence of possible respiratory pathogens. The presence and number of farm animals within 1 km of the patients' home address were assessed using geographic information system (GIS) and were compared between cases and age-matched control subjects. Of 408 patients with CAP, pathogens were detected in 275 (67.4%) patients. The presence of sheep and the number of goats were associated with CAP caused by Coxiella burnetii in a multiple logistic regression model (P < 0.05). CAP with unknown aetiology was not associated with the presence of animal farms (P > 0.10). The use of GIS in combination with aetiology of CAP could be potentially used to target diagnostics and to identify outbreaks of rare zoonotic disease.

Coxiella burnetii infections in sheep or goats: an opinionated review

Q fever is an almost ubiquitous zoonosis caused by Coxiella burnetii, which is able to infect several animal species, as well as humans. Cattle, sheep and goats are the primary animal reservoirs. In small ruminants, infections are mostly without clinical symptoms, however, abortions and stillbirths can occur, mainly during late pregnancy. Shedding of C. burnetii occurs in feces, milk and, mostly, in placental membranes and birth fluids. During parturition of infected small ruminants, bacteria from birth products become aerosolized. Transmission to humans mainly happens through inhalation of contaminated aerosols. In the last decade, there have been several, sometimes large, human Q fever outbreaks related to sheep and goats. In this review, we describe C. burnetii infections in sheep and goats, including both advantages and disadvantages of available laboratory techniques, as pathology, different serological tests, PCR and culture to detect C. burnetii. Moreover, worldwide prevalences of C. burnetii in small ruminants are described, as well as possibilities for treatment and prevention. Prevention of shedding and subsequent environmental contamination by vaccination of sheep and goats with a phase I vaccine are possible. In addition, compulsory surveillance of C. burnetii in small ruminant farms raises awareness and hygiene measures in farms help to decrease exposure of people to the organism. Finally, this review challenges how to contain an infection of C. burnetii in small ruminants, bearing in mind possible consequences for the human population and probable interference of veterinary strategies, human risk perception and political considerations.

A probably minor role for land-applied goat manure in the transmission of Coxiella burnetii to humans in the 2007-2010 Dutch Q fever outbreak

In 2007, Q fever started to become a major public health problem in the Netherlands, with small ruminants as most probable source. In order to reduce environmental contamination, control measures for manure were implemented because of the assumption that manure was highly contaminated with Coxiella burnetii. The aims of this study were 1) to clarify the role of C. burnetii contaminated manure from dairy goat farms in the transmission of C. burnetii to humans, 2) to assess the impact of manure storage on temperature profiles in dunghills, and 3) to calculate the decimal reduction time of the Nine Mile RSA 493 reference strain of C. burnetii under experimental conditions in different matrices. For these purposes, records on distribution of manure from case and control herds were mapped and a potential relation to incidences of human Q fever was investigated. Additionally, temperatures in two dunghills were measured and related to heat resistance of C. burnetii. Results of negative binomial regression showed no significant association between the incidence of human Q fever cases and the source of manure. Temperature measurements in the core and shell of dunghills on two farms were above 40°C for at least ten consecutive days which would result in a strong reduction of C. burnetii over time. Our findings indicate that there is no relationship between incidence of human Q fever and land applied manure from dairy goat farms with an abortion wave caused by C. burnetii. Temperature measurements in dunghills on two farms with C. burnetii shedding dairy goat herds further support the very limited role of goat manure as a transmission route during the Dutch human Q fever outbreak. It is very likely that the composting process within a dunghill will result in a clear reduction in the number of viable C. burnetii.

Q fever in the Netherlands - 2007-2010: what we learned from the largest outbreak ever

Q fever is a zoonosis caused by Coxiella burnetii with a presentation ranging from asymptomatic seroconversion to possibly fatal chronic Q fever. The Netherlands faced an exceptionally large outbreak of Q fever from 2007 to 2010: 4026 human cases were notified, which makes it the largest Q fever outbreak ever reported. This outbreak, because of its size, allowed collecting a wide range of information on the natural history of Q fever, as well as on its transmission and clinical presentation. It also posed unprecedented public healthcare problems, especially for the concomitant management of the epizootic by veterinarian authorities and public health authorities, but also for the management of transmission risk related to blood donation. The need for cost efficient measures emerged rapidly because of the great number of infected individuals or at risk of infection, with a need for guidance on follow-up of acute Q fever patients, screening of pregnant women, or implementation of diagnostic algorithms. The acute outbreak was controlled by drastic veterinarian measures but chronic Q fever will remain a problem for the coming years.

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.

Spatial analysis of positive and negative Q fever laboratory results for identifying high- and low-risk areas of infection in the Netherlands

Background

The Netherlands faced a large Q fever epidemic from 2007 to 2010, in which thousands of people were tested for the presence of antibodies against Coxiella burnetii as part of individual patient diagnosis. So far, only data of notified cases were used for the identification of high-risk areas, which can lead to misclassification of risk. Therefore, we identified high- and low-risk areas based on laboratory test results to make control measures more efficient.

Methods

Data on diagnostic Q fever laboratory tests were obtained from two regional laboratories of medical microbiology in the high-incidence area in the south of the Netherlands. The proportion of patients testing positive was mapped per postal code area. Patients testing positive were compared to patients testing negative based on the distance between residential address and the nearest infected goat farm with adjustment for age and sex.

Results and conclusion

Of 11,035 patients tested, 4,011 (36.4%) had a positive laboratory test result for Q fever. Maps showing the spatial pattern of tests performed and proportion of positive tests allowed for the identification of high- and low-risk Q fever areas. The proportion of patients testing positive was higher in areas close to infected goat farms compared to areas further away. Patients living <1 km from an infected goat farm had a substantially higher risk of testing positive for antibodies to C. burnetii than those living >10 km away (OR 21.70, 95% CI 16.28–28.92). Laboratory test results have the potential to make control measures more efficient by identifying high-risk areas as well as low-risk areas.