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 km2 and 8 km2 and a 6-variable BioClim data set at 8 km2. Future models suggest large areas predicted under current conditions may be reduced by 2050 with the A2 model predicting ∼14–16% loss across the three spatial resolutions. There was greater variability in the B2 models across scenarios predicting ∼15% loss at 55 km2, ∼34% loss at 8 km2, and ∼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 km2. 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.

BackgroundBacillus 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.ResultsThe 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.ConclusionsOur 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.

This study provides useful baseline data for guiding future disease control programs.

We describe the genetic diversity of 1327 Brucella strains from human patients in Kazakhstan using multiple-locus variable-number tandem repeat (VNTR) analysis (MLVA). All strains were assigned to the Brucella melitensis East Mediterranean group and clustered into 16 MLVA11 genotypes, nine of which are reported for the first time. MLVA11 genotype 116 predominates (86.8%) and is present all over Kazakhstan indicating existence and temporary preservation of a “founder effect” among B. melitensis strains circulating in Central Eurasia. The diversity pattern observed in humans is highly similar to the pattern previously reported in animals. The diversity observed by MLVA suggested that the epidemiological status of brucellosis in Kazakhstan is the result of the introduction of a few lineages, which have subsequently diversified at the most unstable tandem repeat loci. This investigation will allow to select the most relevant strains for testing these hypotheses via whole genome sequencing and to subsequently adjust the genotyping scheme to the Kazakhstan epidemiological situation.

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