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

Abstract

Abstract. 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 (G i *) statistic and a multidirectional optimal ecotope algorithm (AMOEBA) were compared using 1 st , 2 nd and 3 rd order Rook contiguity matrices. Multivariate statistical tests were used to evaluate the environmental signatures between clusters and non-clusters from the AMOEBA and G i * 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, G i * 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 G i * clusters. In general, AMOEBA predicted more irregularly shaped clusters of outbreaks in both livestock groups, while G i * 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.

Modeling the Ecological Niche of Bacillus anthracis to Map Anthrax Risk in Kyrgyzstan

“…The steppe region of Talas reaches north and west into the Zhambyl area of Kazakhstan, previously defined as suitable B. anthracis habitat where hot spots of anthrax transmission occurred in cattle and small ruminants (sheep and goat) during a 40-year period. 7 The little research on anthrax in Uzbekistan predicted the Fergana Valley, in the east, as suitable for B. anthracis. 35 This valley leads into west central Kyrgyzstan, where we define the Osh anthrax focus (Osh, Jalal-Abad, and Batken oblasts).…”

“…10 ENM approaches have been used to predict the distribution of B. anthracis across several landscapes under current 9,[11][12][13] and future climatic conditions. 9,14 Such models provide a "first estimate" of where the pathogen may persist and Kracalik and others 7 suggested national passive surveillance should include the appropriate diagnostic tools and regional veterinary training to properly detect outbreaks in areas predicted by ENMs.…”

“…2 However, in resource-limited areas, widespread vaccination may be financially prohibitive and untenable. Of particular concern are agrarian and resource-limited countries in the Caucasus [2][3][4] and Central Asia, [5][6][7] where independence from the former Soviet Union has hindered public health management because of decreased funding. 5 In Central Asia, anthrax represents a major veterinary and public health concern 5,7,8 .…”

“…Of particular concern are agrarian and resource-limited countries in the Caucasus [2][3][4] and Central Asia, [5][6][7] where independence from the former Soviet Union has hindered public health management because of decreased funding. 5 In Central Asia, anthrax represents a major veterinary and public health concern 5,7,8 . In the Republic of Kyrgyzstan, ongoing anthrax outbreaks have been reported in humans associated with handling infected livestock and contaminated animal by-products such as meat or hides.…”

Identifying hotspots of human anthrax transmission using three local clustering techniques

“…This approach differs from a traditional Getis-Ord statistic, which is traditionally employed with a search defined by a circle of radius d (set in map units) or using a contiguity matrix defining neighbor connections based on a weights matrix. AMOEBA has been used to identify cluster shapes of socioeconomic and physical environmental factors to identify continuous groupings or neighborhoods of characteristics (Weeks, Getis, Hill, Agyei-Mensah, & Rain, 2010), and for the detection of significant local clusters of livestock anthrax in Kazakhstan (Kracalik et al, 2012).…”