Mapping the Dynamics of Livestock Brucellosis in Georgia
Using Serological Data and Spatial Statistics
E. Mamisashvili1, T. Onashvili 1 L. Kerdzevadze1, K. Goginashvili 1, T.Tigilauri1, M. Donduashvili 1 , M. Nikolaishvili 1 ,
I. Beradze 1, M. Zakareishvili, M. Kokhreidze, M. Gelashvili1, Ian Kracalik2, S. Elizabeth Rácz3, and Jason K. Blackburn2
1Laboratory
of the Ministry of Agriculture, Tbilisi, Georgia; 2 Spatial Epidemiology and Ecology Research Lab, Department of Geography and Emerging Pathogens
Institute, University of Florida, Gainesville, FL, 32611, U.S.A.;3 H.W. Manter Laboratory, University of Nebraska-Lincoln, Lincoln, Nebraska, 65855 U.S.A
Introduction
Results Continued
Spatial Analysis continued
In Georgia, brucellosis is the most common of all bacterial zoonoses affecting animal populations and resulting in significant
economic losses. Multiple species of Brucella contribute to chronic health complications in both animal and human populations in
the country. Monitoring the occurrence of brucellosis is not only crucial for mitigating agricultural losses brought on by the disease,
but also for controlling impacts on human populations as well. Areas of the former Soviet Union have been burdened by some the
highest incidences of human brucellosis in world (Pappas et al. 2006). Therefore, implementing surveillance strategies on livestock
populations could potentially have a two fold impact; reduced economic losses and improved rates of disease. Recent research in
Armenia has shown that implementing spatial analyses in epidemiological investigations of veterinary health provided vital insight
into the spatial distribution of livestock brucellosis and also aided in indentifying factors that may contribute to the presence of the
disease (Porphyre et al. 2010). Similar research in Mexico was also able to elucidate risk factors for brucellosis among livestock by
utilizing statistical methodologies (Mikolon et al. 1998).
In addition to generating density surfaces for SP’s and SN’s a surface displaying the smoothed odds of Brucella. Spp. was created using the
density of SN’s as the denominator and the density of SP’s as the numerator. This identified areas where the odds of Brucella. Spp. were the
highest (Stevenson et al. 2005). Locating areas on the landscape where cases are higher than expected may be beneficial for initiating
interventions and public health management strategies. In order to determine whether or not there was clustering or a higher than
expected number of cases (SP ‘s) in relation to controls (SN’s) at the village level, a Bernoulli model was employed within the SaTScan v.9.0
statistical software package (Kulldorff 1997). SaTScan uses a series of overlapping circles up to a predetermined size to statistically identify
areas, which given a certain likelihood contain a higher number of cases in side a circle than outside of the circle. The Bernoulli model was
run using a maximum cluster size of 50% of the population at risk with no geographic overlap allowed .
Results from the Bernoulli model run in SaTScan revealed the presence of statistically significant clusters of SP’s
(Figure 5). The primary cluster for the model was located in the southwestern part of the country and encompassed
an area approximately 80Km in diameter. There were also five secondary clusters identified across the landscape. A
secondary cluster encompassing approximately a 7 km area was located in the west while the additional secondary
cluster all were comprised of single locations.
In an effort to build capacity and increase public health infrastructure geographic information systems (GIS) and global
positioning systems were used in conjunction with sampling efforts in order to detail a baseline prevalence of brucellosis across
selected regions of Georgia. Recent advances in GIS and GPS have greatly increased the ability of users to incorporate advanced
analyses and data management. As part of a collaborative effort to initialize brucellosis surveillance during the spring and fall
seasons of 2008 – 2010, the Laboratory of the Ministry of Agriculture (LMA) sampled livestock in three regions (provinces) of
Georgia including Kakheti, Kvemo Kartli and Imereti and consisting of 16 administrative rayons.
The primary goals of this study were the following: 1) to conduct surveillance for livestock brucellosis across specific rayons
of Georgia designated as high risk zones 2) to enhance the capacity and knowledge of veterinary and public health management in
order to adopt appropriate strategies for continued vigilance 3) to incorporate GIS and GPS technology into surveillance strategies in
order to analyze spatial patterns in the distribution of brucellosis through the use of advanced spatial analytical techniques and 4)
to apply new diagnostic methods.
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254
Figure 2. Distribution of seropositives and seronegatives by rayon across Georgia. Map in
red displays the total number of seropositives by rayon while the map in blue displays
the total number of seronegatives by rayon. The bottom map illustrates the ratio of
seropositives to seronegatives as a percent.
109
3224
1985
Sero Positive
Sero Negative
Kakheti
212
3224
Kvemo Kartli
254
1985
1648
Imereti
109
1648
1
0
Tbilisi
1
0
Figure 1. Total number of seropositive and seronegative samples obtained from collection efforts in major
regions of Georgia. The bar chart area in purple show the number of seronegatives collected in each
region, while are in pink shows the number of seropositive collected in each area.
Figure 5. Map shows spatial clustering using a Bernoulli case /control model in SaTScan. The circle in red
depicts the primary cluster with a radius of 39.46 Km. Secondary clusters are shown with the black circle with
a radius of 3.47 Km and red stars, which depict clusters at s ingle location. Green dots represent the location of
seropositives while yellow dots represent seronegatives.
Results
The proprtion of SP’s to SN’s was greatest in the Tskatubo, Tetri Tskaro, and Gardabani rayons (figure 2). Crude and EBS smoothed
prevalence estimates show higher proportions of SP’s in the southwest in the Marneuli and Gurajanni rayons (Figure 3). Higher proportions
were also indicated in the East Central area of the country in the Zestaponi and Terjola rayons. The odds of Brucella spp. were lower in
centralized areas of the sampling while the odds increased along the outer fringes (Figure 4). Kernel density surfaces show a higher density of
SN’ across most of the sampling sites.
Discussion
Identifying areas with an increased occurrence of disease may be useful for public health professionals. The
mapping and analyses performed using the brucellosis seroprevalence study undertaken by scientists at LMA represents the
first stage in elucidating factors that could potentially promote the presence of brucellosis. Spatial analysis of the data
revealed areas with increased crude and smoothed prevalence estimates. These estimates may be actual rayons with an
increased presence of the disease or they could be an artifact of the sampling effort. The sampling effort may also affect the
Kernel density estimation, which showed a higher odds of Brucella spp. , although the density of SN’s was greater in most
locations. The proportion of SP’s to SN’s shown in Figure 2 may reveal why the odds of Brucella spp. were higher in some
Materials and Methods
From July 2008 through January 2011 a team from LMA collected animal specimens seasonally (Spring, Fall) in three
regions designated: Kakheti, Kvemo Kartli and Imereti (Figure 1). In total the field sampling efforts yielded 4430 blood/ serum
samples and 1597 milk samples from cattle; 2522 bloods/serum samples, and 233 milk samples from sheep; and 811blood/serum
samples and210 milk samples from goats. Testing of samples was performed using serology and molecular diagnostics (RoseBengal, Bacteriological, RT-PCR, and AMOS PCR). In total 552 serological tests were positive on Rose-Bengal. An additional 20
bacterial isolates were recovered and identified as Brucella melitensis n=8 or Brucella abortus n=12 obtained from AMOS PCR
across the 16 rayons.
locals. The cluster results from the SaTScan were successful in locating areas of concern or key locations that public health
Spatial Analysis
of disease to human populations.
professional may want to target for additional research. Future studies may incorporate risk factors that are associated with
the presence of brucellosis in livestock in order to better assess spatial differences in the level of possible exposures.
Furthermore, case-control studies could potentially be developed that revisit locations of SP’s and SN’s to examine potential
exposures. Overall these analysis were useful in establishing baseline rates of brucellosis and identifying potential areas to
target in control efforts. Understanding the distribution of livestock patterns is the first step in helping to prevent the spread
Latitude and longitude pairs identifying the location of seropositive (SP) and seronegative (SN) cases by were recorded to
the nearest village. The total number of positives and negatives were aggregated to the rayon level and the ratio the of positives
to negative per were rayon were calculated as a percent (Figure 2). Crude prevalence estimates per 100,000 were taken and
calculated as the total number of from SP’s in a rayon divided by the estimated livestock population including small ruminants
sheep and goats and cattle. Smoothed prevalence estimates were also calculated using the Empirical Bayesian smoother (EBS) in
the GeoDa software package (Anselin et al. 2006). The EBS algorithm adjusts for large population variances that may lead to
unstable prevalence estimates. To describe the spatial distribution of sampling SP’s and SN’s a Kernel density estimation was used
to visualize a smoothed density surface . The optimal bandwidth for the Kernel density estimation was chosen following
Fotheringham et al. 2000:
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Figure 3. Crude prevalence estimates per 100,000 are shown in map A and
Empirical Bayes smoothed rates are shown in map B. Estimates were
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Figure 4. Kernel density surfaces for seropositives are shown in map A and for
seronegatives in map B. The odds of Brucella spp. are displayed in map C.
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Acknowledgements
This Cooperative Biological Research project was funded by the United States Defense Threat Reduction Agency (DTRA) as part of the
Biological Threat Reduction Program in Georgia. UF funding is administered through the Joint University Partnership under the
University of New Mexico.