Kazakh Science Center for Quarantine and Zoonotic Diseases

Almaty, Kazakhstan

Kazakh Science Center for Quarantine and Zoonotic Diseases

Almaty, Kazakhstan
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Mullins J.,University of Florida | Lukhnova L.,Kazakh Science Center for Quarantine and Zoonotic Diseases | Pazilov Y.,Kazakh Science Center for Quarantine and Zoonotic Diseases | Van Ert M.,University of Florida | Blackburn J.K.,University of Florida
BMC Ecology | Year: 2011

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. © 2011 Mullins et al; licensee BioMed Central Ltd.

Kracalik I.T.,University of Florida | Blackburn J.K.,University of Florida | Lukhnova L.,Kazakh Science Center for Quarantine and Zoonotic Diseases | Pazilov Y.,Kazakh Science Center for Quarantine and Zoonotic Diseases | Hugh Jones M.E.,Louisiana State University
Geospatial Health | Year: 2013

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 MannWhitney 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.

Kracalik I.,University of Florida | Lukhnova L.,Kazakh Science Center for Quarantine and Zoonotic Diseases | Aikimbayev A.,Republican Sanitary and Epidemiological Station | Pazilov Y.,Kazakh Science Center for Quarantine and Zoonotic Diseases | And 2 more authors.
Spatial and Spatio-temporal Epidemiology | Year: 2011

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. © 2010 Elsevier Ltd.

Joyner T.A.,University of Florida | Lukhnova L.,Kazakh Science Center for Quarantine and Zoonotic Diseases | Pazilov Y.,Kazakh Science Center for Quarantine and Zoonotic Diseases | Temiralyeva G.,Kazakh Science Center for Quarantine and Zoonotic Diseases | And 3 more authors.
PLoS ONE | Year: 2010

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 km2 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 ∼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 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. © 2010 Joyner et al.

Mullins J.C.,University of Florida | Garofolo G.,Instituto Zooprofilattico Sperimentale Dellabruzzo E Del Molise G Caporale | Garofolo G.,Anthrax Reference Institute of Italy | Van Ert M.,University of Florida | And 4 more authors.
PLoS ONE | Year: 2013

We modeled the ecological niche of a globally successful Bacillus anthracis sublineage in the United States, Italy and Kazakhstan to better understand the geographic distribution of anthrax and potential associations between regional populations and ecology. Country-specific ecological-niche models were developed and reciprocally transferred to the other countries to determine if pathogen presence could be accurately predicted on novel landscapes. Native models accurately predicted endemic areas within each country, but transferred models failed to predict known occurrences in the outside countries. While the effects of variable selection and limitations of the genetic data should be considered, results suggest differing ecological associations for the B. anthracis populations within each country and may reflect niche specialization within the sublineage. Our findings provide guidance for developing accurate ecological niche models for this pathogen; models should be developed regionally, on the native landscape, and with consideration to population genetics Further genomic analysis will improve our understanding of the genetic-ecological dynamics of B. anthracis across these countries and may lead to more refined predictive models for surveillance and proactive vaccination programs. Further studies should evaluate the impact of variable selection of native and transferred models. © 2013 Mullins et al.

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