Materials and Methods Tularemia Occurrence Data

1. Tularemia in Georgia :1946-2009 Nikoloz Tsertsvadze1, Lela Bakanidze1, Paata Imnadze1, Shota Tsanava1, Julieta Manvelyan1, Ian Kracalik2, S. Elizabeth Rácz3, and Jason K. Blackburn2 1National Center for Disease Control of Georgia, 9, M.Asatiani str. 0177, 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 W-529 Nebraska Hall, H.W. Manter Laboratory, University of Nebraska-Lincoln, Lincoln, Nebraska, 65855 U.S.A. Results The location of training and independent data is shown in figure 2. Training data were used for model-building and independent data were used for model validation. The current potential distribution of F. tularensis is shown in figure 3 and it was created in GARP using tularemia isolate data obtained from Dermacentor marginatus and eight environmental variables shown in Table 1. Accuracy metrics were created after the model-building process using independent data and are shown in Table 2. Areas of highest model agreement shown in red were located in the in the central portion of the country. The SaTScan space-time permutation model indicated the presence of statistically significant space-time clusters (Figure 4). In total three clusters were identified: one primary cluster and two secondary clusters. The primary cluster located in the center part of the country consisted of a radius of 7.83Km ranging in time from 2007 to 2010. The two secondary clusters were 4.85km ranging in time from 1990 to 1991 and 15.26Km ranging in time from 1981 to 1982 respectively. Spatial Analysis The space-time permutation model in the SaTScan software package (Kulldorff et al. 2005) was used to analyze the presence of clustering among the isolate samples collected. This method analyzes the distribution of events retrospectively to identify space-time clustering of events in the absence of population data. SaTScan uses a series of thousands or millions 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 compared to outside of the circle. Introduction Tularemia is a disease considered to be widely distributed across the Northern Hemisphere (Petersen 2005). Tularemia, caused by the gram-negative bacterium Francisella tularensis, is endemic in small mammals (rodents, insectivores, rabbits and hares) and transmitted by arthropod vectors (ticks, fleas) in Georgia. Additionally, surveillance data also shown isolates from water, wheat, and avian emeses. Tularemia investigations in Georgia began in the mid-1940's, and initially helped to establish the existence of autonomous natural foci: one in the mountainous region of Meskhet-Javakheti and a second in the Kartl-Kakheti valley. The main reservoir is Microtus arvalis transcaucasicus (common vole) and the principal arthropod vector is Dermacentor marginatus (sheep tick). In order to increase the capacity and efficiency of public health surveillance in Georgia geographic information systems (GIS) were used to help identify spatial epidemiological characteristics of tularemia. Uncovering factors related to the transmission and epidemiology of tularemia may provide critical information regarding control of the disease to public health officials and the general population. Incorporating ecological niche models (ENMs) and spatial statistical models into investigations of disease occurrence is one way of elucidating aspects related to a disease’s presence. Several studies have used ENMs to describe the ecological distribution of disease causing agents (Blackburn et al. 2007, Joyner et al. 2010). One such modeling technique known as the Genetic Algorithm for Rule Set Production (GARP) uses known species occurrence data in conjunction with environmental and climatic data to predict areas on the landscape that may potentially support the presence of a species. Recent research in the United States has attempted to map and describe the potential geographic distribution of F. tularensis using an ENM known as the Genetic Algorithm for Rule Set Production (GARP) and environmental data to identify shifting patterns of the predicted distribution in relation to climatic change (Nakazawa et al. 2007). In this study researches found that climatic and environmental characteristics may be strong predictors of the presence of F. tularensis. In addition to ENMs spatial statistical methodlogies may also be used to identify areas that may have a higher presence of a disease. The SaTScan spatial statistic developed by Kulldorff (1997) has been used in numerous studies to locate clusters or a high occurrence of disease (Coleman et al. 2009). Areas with a higher than expected distribution of a pathogen may represent areas with an increased risk for infection therefore, may aid in directing public health officials in establishing control and prevention guidelines. The purpose of this study was to describe spatial epidemiological characteristics of F. tularensis in Georgia. Table 1. Environmental variables used during the model-building process Table 2. Accuracy metrics for the potential distribution of Dermacentor marginatus based on the GARP experiment Discussion Francisellatularensis,while rare in humans, has an established natural ecology within Georgia. A species-specific ecological niche model of Dermacentor marginatus, a primary vector of the pathogen, indicates the potential geographic locations where the bacterium and vector maybe present on the landscape (Figure 3). This modeling experiment represents only one of the potential vectors for the disease. However, this may allow for more direct intervention strategies by targeting a single vector/host. Areas indicated by the model that promote the occurrence of the vector and pathogen could be used by public health officials to inform vector control and the public of potential risks. In addition to providing an increased awareness of the potential presence of the vector/pathogen, the ENM can be utilized to direct further surveillance efforts. Areas not previously sampled, but identified in the model as potentially harboring the vector and bacterium can be used to direct targeted sampling/collection activities in the future. Space-time patterns of tularemia isolates indicate relatively short temporal and distinct spatial cluster locations durations during the 54-year study period. Clusters in the center of the country while temporally divergent were located adjacent to each other revealing a potential localized focus of tularemia (Figure 4). Although clusters were statistically significant they could have been a result of directed sampling efforts or fluctuations in specific vector/host populations. The space-time permutation does not take into account the underlying population therefore clusters are calculated in proportion to the entire sample distribution. Despite the lack of population estimates the identification of clusters may help target high risk areas. Using niche modeling strategies in conjunction with spatial clustering techniques could help in identify areas of concern. In this study SaTScan and GARP both identified similar areas of interest in the center of the country. This combination of spatial methodologies can aid in the validation of mapping high risk areas. In addition to modeling more vectors of the bacterium, future studies could examine the potential effects of climate change on the distribution of Dermacentor marginatus. Additionally, further studies should focus on modeling the larger group of host and vector species to develop a more complete understanding of the distribution of this pathogen and its transmission dynamics in Georgia. * AUC = area under curve † N was divided into 50% training/50% testing at each model iteration § p < 0.001 * Normalized Difference Vegetation Index Figure 3. The potential geographic distribution of Dermacentor marginatus in Georgia based on an eight variable ecological niche model. The color ramp represents model agreement from the best subset routine, with darker red colors reflecting higher model agreement, or greater confidence in the prediction of actual bacteria habitat. Yellow and green dots represent model training and testing data, respectively. Figure 2. Testing and training data of F. tularensisisolates used in the GARP modeling algorithm. Figure 1. The distribution of F. tularensisisolates identified by the sample origin. References Brotons L, Thuiller W, Araujo MB, Hirzel AH (2004) Presence-absence versus presence-only modelling methods for predicting bird habitat suitability. Ecography 27: 437-448. Coleman, Marlize, Michael Coleman, Aaron M. Mabuza, et al. 2009. Using SaTscan method to detect local malaria clusters for guiding control programs. Malaria Journal. 68(8). Hay, S.I., A.J. Tatem, A.J. Graham, S.J. Goetz, and David Rogers. (2006).Global environmental data for mapping infectious disease distribution. Advances in Parasitology. 62:37-77. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978. Joyner, T.A., L. 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Journal of Clinical Microbiology 38(10): 3780-3784. Stockwell DRB, Peters D (1999) The GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science 13(2): 143-158. Sweeney AW, Beebe NW, Cooper RD, Bauer JT, Peterson AT (2006) Environmental factors associated with distribution and range limits of malaria vector Anopheles farauti in Australia. Journal of Medical Entomology 43(5): 1068-1075. Materials and Methods Tularemia Occurrence Data A database totaling 93 strains was constructed from records between 1946 and 2010 archived at the National Centers for Disease Control (NCDC), Tbilisi, Georgia. Samples were collected across 11 rayons and obtained from several sources (Figure 1). Field sites were surveyed by scientists from NCDC and samples collected via X,Y latitudinal and longitudinal pairs georeferenced to the nearest village. Diagnoses were carried out serologically, specifically, by identifying an antibody titer in the blood serum of a patient. Isolation of tularemia causative agent was performed, using bioprobes and on McCoy egg-yolk medium. Improvements in diagnostics now include isolation of strains with cysteine-added chocolate agar, ELISA, and RT-PCR techniques. Environmental and Climate Variables data Current climate grid data were freely downloadable (www.worldclim.org) on the WORLDCLIM website (Hijmans et al. 2005) or provided by (Hay et al. 2006). A resolution of 8 km was utilized for this study because village latitude and longitude coordinates were occasionally estimated to be greater than 1 km away from farms where a tularemia isolate was obtained. Ecological Niche Modeling and Spatial Modeling For this study, one modeling scenario was employed to examine the current geographic distribution of F. tularensis. The scenario contained eight environmental variables that described temperature, precipitation, vegetation, and elevation to create a model of the potential current distribution of F. tularensis. We used the GARP ecological niche modeling program to build the current predictive model for Georgia. GARP is a presence-only genetic algorithm that models species’ potential geographic distributions through an iterative process of training and testing that occurs through resampling and replacement of input data (Stockwell and Peters 1999). A pattern matching process is applied that finds non-random relationships between species localities and specific variables that describe the environment. These relationships are written as a series of if/then logic statements (known as rules) that define whether conditions within the rule are defining presence or absence. 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. Figure 4 SaTScan space-time permutation model identifying significantly high space-time clusters. The red circle represents the primary cluster identified by SaTScan with a radius of 7.83Km and lasting from 2007 to 2010. Secondary clusters are shown in blue 15.26Km (1981 to 1982) and in black 4.85Km (1990 to 1991).