Modeling the Potential Distribution of Bacillus anthracis under Multiple Climate Change Scenarios for Kazakhstan

Abstract

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.

Anthrax Outbreaks in Bangladesh, 2009–2010

“…If one considers the scarcity of resources, the vaccination strategy should identify target areas for vaccination based on the understanding of the spatial ecology and the geographic distribution of B. anthracis in Bangladesh, as has been done in other settings. 28,29 Because some human cases were reported more than one month after the onset of illness, imperfect recall by the case-patients might be a reason that 24 (9%) of the 273 cutaneous anthrax case-patients did not report a history of any of the exposures related to contact with a sick animal, meat of a sick slaughtered animal, or animal by-products. There might also have been some unknown exposure because the surrounding environment was contaminated by disposed animal carcasses, and stable flies and mosquitoes may play a role in transmission of animal and human anthrax.…”

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

“…These data were obtained from a larger database development effort in collaboration with the Kazakh Science Center for Quarantine and Zoonotic Disease in Almaty, Kazakhstan, described in detail elsewhere (Aikembayev et al, 2010;Joyner et al, 2010;Kracalik et al, 2011). The data were divided into livestock groups based on results of Aikimbayev et al (2010), which suggested that cases within each group where distributed differently. Broadly, this subset of data reflects the time period after mass vaccination was implemented and corresponds to the averaged climate data from the WorldClim data set use in the environmental analysis (see below).…”

Modeling of Wildlife-Associated Zoonoses: Applications and Caveats

“…Recently, GARP rules have been used to build and graph climatic envelopes for a specific genetic sub-lineage of Bacillus anthracis in Kazakhstan, compared to models built from larger data sets representing outbreaks regardless of genotype (Mullins et al 2011). This process also allows the user to project models onto landscapes where occurrence data are unavailable, such as when surveillance or reporting are lacking (Blackburn 2010), or onto the same landscape in future time periods to evaluate the effects of climate change (Holt et al 2009;Blackburn 2010;Joyner et al 2010).…”