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.

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).