Evaluation of spatial brucellosis distribution using the Geographic Information System: Towards building a high performance spatial epidemiology system for supervision on zoonotic infections

Brucellosis, a bacterial zoonosis, remains a public health threat worldwide, particularly in Kazakhstan and Central Asia. The disease can manifest in a variety of forms, from asymptomatic to severe organic forms. The republic is still included into ten countries with the highest incidence levels for the first time diagnosed human brucellosis, which emphasizes the magnitude of the problem. This study was conducted using Geographic Information System (GIS) techniques, in order to evaluate the spatial distribution of the disease in the rural area of Almaty and Zhambyl oblasts (provinces). The spatial distribution of Brucella melitensis biovars was studied for 2011-2013 (continuous sampling), as well as the incidence rate of the first time diagnosed human brucellosis and the prevalence of this infection in livestock, per long-time annual average values. For the data visualization we use free software, QuantumGIS of versions 1.8.0 (Lisboa) and 2.0.1 (Dufour). A GIS-based spatial analysis was carried out to identify geographic distribution patterns of brucellosis infection in humans and animals. Local Moran's I statistic was employed using GeoDa software to evaluate cluster analysis. Incidence rates were empirically Bayes smoothed for human, cattle, and a combined sheep/goat group. Results of the spatial autocorrelation analysis for 2007-2013 showed high incidence rates for humans and animals were geographically clustered in the southwest region, where human clusters decreased in later years. While this may reflect an improving human disease situation or only annual reporting differences, these results suggest that human and animal incidence is closely associated. The use of spatial autocorrelation proved a great benefit for better visualization of the endemic, especially in elucidating the actual distribution of brucellosis cases in the rayons (districts) and providing an effective tool for planning the monitoring of the disease at a local level.