Assays for Identification and Differentiation of Brucella Species: A Review.

Brucellosis is one of the most important and widespread bacterial zoonoses worldwide. Cases are reported annually across the range of known infectious species of the genus Brucella. Globally, Brucella melitensis, primarily hosted by domestic sheep and goats, affects large proportions of livestock herds, and frequently spills over into humans. While some species, such as Brucella abortus, are well controlled in livestock in areas of North America, the Greater Yellowstone Ecosystem supports the species in native wild ungulates with occasional spillover to livestock. Elsewhere in North America, other Brucella species still infect domestic dogs and feral swine, with some associated human cases. Brucella spp. patterns vary across space globally with B. abortus and B. melitensis the most important for livestock control. A myriad of other species within the genus infect a wide range of marine mammals, wildlife, rodents, and even frogs. Infection in humans from these others varies with geography and bacterial species. Control in humans is primarily achieved through livestock vaccination and culling and requires accurate and rapid species confirmation; vaccination is Brucella spp.-specific and typically targets single livestock species for distribution. Traditional bacteriology methods are slow (some media can take up to 21 days for bacterial growth) and often lack the specificity of molecular techniques. Here, we summarize the molecular techniques for confirming and identifying specific Brucella species and provide recommendations for selecting the appropriate methods based on need, sensitivity, and laboratory capabilities/technology. As vaccination/culling approaches are costly and logistically challenging, proper diagnostics and species identification are critical tools for targeting surveillance and control.

Analysing the spatial patterns of livestock anthrax in Kazakhstan in relation to environmental factors: a comparison of local (Gi*) and morphology cluster statistics.

We compared a local clustering and a cluster morphology statistic using anthrax outbreaks in large (cattle) and small (sheep and goats) domestic ruminants across Kazakhstan. The Getis-Ord (Gi*) statistic and a multidirectional optimal ecotope algorithm (AMOEBA) were compared using 1st, 2nd and 3rd order Rook contiguity matrices. Multivariate statistical tests were used to evaluate the environmental signatures between clusters and non-clusters from the AMOEBA and Gi* tests. A logistic regression was used to define a risk surface for anthrax outbreaks and to compare agreement between clustering methodologies. Tests revealed differences in the spatial distribution of clusters as well as the total number of clusters in large ruminants for AMOEBA (n = 149) and for small ruminants (n = 9). In contrast, Gi* revealed fewer large ruminant clusters (n = 122) and more small ruminant clusters (n = 61). Significant environmental differences were found between groups using the Kruskall-Wallis and Mann-Whitney U tests. Logistic regression was used to model the presence/absence of anthrax outbreaks and define a risk surface for large ruminants to compare with cluster analyses. The model predicted 32.2% of the landscape as high risk. Approximately 75% of AMOEBA clusters corresponded to predicted high risk, compared with ~64% of Gi* clusters. In general, AMOEBA predicted more irregularly shaped clusters of outbreaks in both livestock groups, while Gi* tended to predict larger, circular clusters. Here we provide an evaluation of both tests and a discussion of the use of each to detect environmental conditions associated with anthrax outbreak clusters in domestic livestock. These findings illustrate important differences in spatial statistical methods for defining local clusters and highlight the importance of selecting appropriate levels of data aggregation.

Ecological niche modelling of the Bacillus anthracis A1.a sub-lineage in Kazakhstan.

BACKGROUND: Bacillus anthracis, the causative agent of anthrax, is a globally distributed zoonotic pathogen that continues to be a veterinary and human health problem in Central Asia. We used a database of anthrax outbreak locations in Kazakhstan and a subset of genotyped isolates to model the geographic distribution and ecological associations of B. anthracis in Kazakhstan. The aims of the study were to test the influence of soil variables on a previous ecological niche based prediction of B. anthracis in Kazakhstan and to determine if a single sub-lineage of B. anthracis occupies a unique ecological niche. RESULTS: The addition of soil variables to the previously developed ecological niche model did not appreciably alter the limits of the predicted geographic or ecological distribution of B. anthracis in Kazakhstan. The A1.a experiment predicted the sub-lineage to be present over a larger geographic area than did the outbreak based experiment containing multiple lineages. Within the geographic area predicted to be suitable for B. anthracis by all ten best subset models, the A1.a sub-lineage was associated with a wider range of ecological tolerances than the outbreak-soil experiment. Analysis of rule types showed that logit rules predominate in the outbreak-soil experiment and range rules in the A1.a sub-lineage experiment. Random sub-setting of locality points suggests that models of B. anthracis distribution may be sensitive to sample size. CONCLUSIONS: Our analysis supports careful consideration of the taxonomic resolution of data used to create ecological niche models. Further investigations into the environmental affinities of individual lineages and sub-lineages of B. anthracis will be useful in understanding the ecology of the disease at large and small scales. With model based predictions serving as approximations of disease risk, these efforts will improve the efficacy of public health interventions for anthrax prevention and control.

Incorporating retrospective clustering into a prospective Cusum methodology for anthrax: evaluating the effects of disease expectation.

We analysed livestock anthrax in Kazakhstan from 1960-2006, using a prospective CUSUM to examine the affects of expectation on the detection of spatio-temporal clusters. Three methods for deriving baselines were used for CUSUM; a standard z-score, AVG, a spatially-weighted z-score derived from Local Moran's I, LISA, and a moving-window average, MWA. LISA and AVG elicited alarm signals in the second year that did not return below threshold during the 47-year period, while MWA signaled an alarm at year four and relented at year fifteen. The number of spatial clusters elicited varied: LISA n=16, AVG n=11, and MWA n=3, although there were clusters present around Shymkent, in south-central Kazakhstan, in each method. The results illustrate that the selection of a baseline with an unknown background population has a significant effect on the ability to detect the onset of clusters in space and in time when employing a CUSUM methodology.

Modeling the potential distribution of Bacillus anthracis under multiple climate change scenarios for Kazakhstan.

Anthrax, caused by the bacterium Bacillus anthracis, is a zoonotic disease that persists throughout much of the world in livestock, wildlife, and secondarily infects humans. This is true across much of Central Asia, and particularly the Steppe region, including Kazakhstan. This study employed the Genetic Algorithm for Rule-set Prediction (GARP) to model the current and future geographic distribution of Bacillus anthracis in Kazakhstan based on the A2 and B2 IPCC SRES climate change scenarios using a 5-variable data set at 55 km(2) and 8 km(2) and a 6-variable BioClim data set at 8 km(2). Future models suggest large areas predicted under current conditions may be reduced by 2050 with the A2 model predicting approximately 14-16% loss across the three spatial resolutions. There was greater variability in the B2 models across scenarios predicting approximately 15% loss at 55 km(2), approximately 34% loss at 8 km(2), and approximately 30% loss with the BioClim variables. Only very small areas of habitat expansion into new areas were predicted by either A2 or B2 in any models. Greater areas of habitat loss are predicted in the southern regions of Kazakhstan by A2 and B2 models, while moderate habitat loss is also predicted in the northern regions by either B2 model at 8 km(2). Anthrax disease control relies mainly on livestock vaccination and proper carcass disposal, both of which require adequate surveillance. In many situations, including that of Kazakhstan, vaccine resources are limited, and understanding the geographic distribution of the organism, in tandem with current data on livestock population dynamics, can aid in properly allocating doses. While speculative, contemplating future changes in livestock distributions and B. anthracis spore promoting environments can be useful for establishing future surveillance priorities. This study may also have broader applications to global public health surveillance relating to other diseases in addition to B. anthracis.

Clinical and epidemiological characteristic of tularemia in Kazakhstan.

From 1950 to 1977, 5049 human tularemia cases were registered that had been associated with a large number of non-immunized people coming to Kazakh tularemia endemic areas from different places of Soviet Union to harvest the grain. Since 1978, the number of tularemia patients has considerably decreased and during 1992-2001 thirty-one human cases were reported. Epidemiological analysis showed that infection was transmitted by a variety of routes, including bites of infected arthropod, ingestion of infected food and water, transfer to mouth by contaminated hands and direct contact from skinning musk-rats and hares. The disease presented mainly as glandular-bubonic (62.5%), bubonic (25.0%) or pulmonary (12.5%) forms.