Materials and Methods Anthrax occurrence data

1. Monitoring and Mapping Anthrax in Livestock from Georgia 2009 - 2010 M. Nikolaishvili1, T. Onashvili1; L. Kerdzevadze1, E. Mamisashvili1, K. Goginashvili1, T.Tigilauri1, M. Zakareishvili1, I. Beradze1, M. Donduashvili1; M. Kokhreidze1, J. Manvelyan2, N. Tsertsvadze2, I. Krackalik3, S. Rácz4, and J. Blackburn3 1 Laboratory of Ministry of Agriculture, 65 Godziashvili Street, Tbilisi 0159, Georgia;2National Center for Disease Control of Georgia, 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. Introduction Bacillus anthracis, the causative agent of anthrax, is a zoonosis of concern throughout many countries of the world, including Georgia. Livestock are particularly susceptible to exposure and infection by grazing in areas where B. anthracis is ecologically established. Secondary infections in humans are often associated with handling or coming into contact with infected animals (Woods et al. 2004). Understanding and controlling the disease in livestock and wildlife is therefore a crucial step in mitigating outbreaks not only in animal populations, but human populations as well. The collapse of the Soviet Union has brought about dramatic changes in public health infrastructure and management in Georgia. Current efforts within the country have been focused on increasing public and veterinary health capacity through increased surveillance and research collaborations. These efforts have been successful in describing B. anthrcis strain diversity in Georgia (Merabishvili et al. 2006) representing an important first step in understanding the dynamics of anthrax outbreaks. Smith et al. suggests that environmental conditions may explain variations in the geographic distributions of different B. anthracis isolates. Additional studies have also used Ecological niche models (ENMs) as a tool to model a species’ ecological and geographic distribution illustrating that the potential geographic distribution of the bacterium is mitigated by a combination of climatic and environmental variables (Blackburn et al. 2007; Joyner et al. 2010). Many different ENM approaches have been utilized for various studies including the presence-absence approach and the presence-only modeling approach (Brotons et al. 2004). ENM’s have been used elsewhere to model other infectious diseases such as chagas and malaria (Peterson et al. 2002; Sweeney et al. 2006). The ecological niche can be defined as those environmental conditions that allow a species to maintain its population without immigration (Grinnell 1917). That definition was later expanded to state that the presence of a species is correlated to quantifiable environmental and biotic variables that promote its survival, or a region in multi-dimensional space that describes states of the environmental variables which are suitable for the species to exist (i.e. a hypervolume of parameters; Hutchinson 1957). An ENM known as the Genetic Algorithm for Rule-set Prediction (GARP) was utilized for this study and it predicts and areas that more closely resembles a fundamental niche. The objective of this study is to determine the potential geographic distributions of B. anthracis in Georgia using a combination of abiotic climatic and environmental variables. Table 2. Accuracy metrics for the potential distribution of B. anthracis using 1 km environmental data and culture isolates Results The location of training and independent data is shown in Figure 1. Training data were used for model-building and independent data were used to calculate model accuracy metrics. The potential distribution of B. anthracis is shown in Figure 2. Accuracy metrics were created after the model-building process using independent data and are shown in table 2. The modeling experiment predicted that much of the Georgian lowlands are likely suitable for B. anthracis survival, with the higher altitude mountains of the south and north limiting the distribution of the species. Recent livestock outbreaks from 2009 – 2010 occurred in the predicted habitat for the pathogen. The distribution of the A3a genotype also matches the distribution of historic environmental samples and recent livestock isolates. All of the locations of the A3a genotypes were predicted by the ecological niche modeling experiment. More work is needed to determine if an A3a sub-lineage specific model would better predict the distribution of the pathogen. Likewise, more typing efforts would be needed to ensure that the environmental data used do not reflect a greater genetic diversity than that published by Merabishvili et al. (2006). Table 1. Environmental variables used during the model-building process * AUC = area under curve † N was divided into 75% training/25% testing at each model iteration § p < 0.01 Discussion Identifying the potential geographic distribution of B. anthracis in Georgia represents an important step in controlling and preventing the occurrence of anthrax in livestock and humans. Renewed interest in the one health initiative has placed an emphasis on combining efforts from both human and veterinary medicine. This study incorporates medically important information from both fields in an attempt to guide health policy and management. The ENMs generated in this study may allow for the more efficient dissemination of vital health safety information as well as prophylaxis, which can be used to aid both animal and human health initiatives. The modeling experiments indicate that B. anthracis has established a natural ecology across many regions of Georgia, primarily along the lowlands of the country. These areas indicate a combination environmental and climatic factors that may promote the occurrence of the bacterium. However, these areas indicated by the modeling experiment do not conclusively indicate that the bacterium will be found. Areas identified as potentially promoting the bacterium could possibly used by health policy makers to inform the public of potential risks and to efficiently distribute appropriate resources. The modeling experiment also successfully identified specific A3a genotypes in the model. Elucidating factors associated with specific strains of B. anthracis may aid help in determining the dynamics of outbreaks. Specific strains of the organism may have an affinity for certain environmental and/or climatic factors. This aspect of ENMs certainly deserves more attention in the future. Figure 2. The potential geographic distribution of Bacillus anthracis 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. Yellow stars represent the geographic distribution of A3a genotypes from the efforts of Merabishvili et al. (2006) using the MLVA-8 genotyping platform of Keim et al. (2000). Figure 1. The topographic and political landscape of Georgia. Yellow dots represent training data for ecological niche modeling experiments. Green dots represent independent testing data for model accuracy metrics. Dots represent Bacillus anthracis isolates recovered from environmental samples by the National Center for Disease Control (NCDC). References Adjemian JCZ, Girvetz EH, Beckett L, Foley JE (2006) Analysis of genetic algorithm for rule-set production (GARP) modeling approach for predicting distributions of fleas implicated as vectors of plague, Yersiniapestis, in California. Journal of Medical Entomology 43: 93-103. Blackburn JK, McNyset KM, Curtis A, Hugh-Jones ME (2007) Modeling the geographic distribution of Bacillus anthracis, the causative agent of anthrax disease, for the contiguous United States using predictive ecologic niche modeling. American Journal of Tropical Medicine and Hygiene 77(6): 1103-1110. 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. Grinnell J (1917) The niche-relationships of the California thrasher. The Auk 34: 427-433. 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. Hutchinson GE (1957) Concluding Remarks.Coldspring Harbor Symposia Quantitative Biology 22: 415-427. Merabishvili, Maya, MerabNatidze, SergoRigvava, et al. (2006). Diversity of Bacillus anthracisin Georgia and of vaccin strains from the former Soviet Union. Appl. Environ. Microbiol. 72(8):5631-5636. Peterson AT, Sanchez-Cordero V, Beard CB, Ramsey JM (2002) Ecologic niche modeling and potential reservoirs for chagas disease, Mexico. Emerging Infectious Diseases 8(7): 662-667. Smith KL, DeVos V, Bryden H, Price LB, Hugh-Jones ME, et al. (2000) Bacillus anthracis diversity in Kruger National Park. 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. Woods CW, Ospanov K, Myrzabekov A, Favorov M, Plikaytis B, et al. (2004) Risk factors for human anthrax among contacts of anthrax-infected livestock in Kazakstan. American Journal of Tropical Medicine and Hygiene 71(1): 48-52. Materials and Methods Anthrax occurrence data A database of anthrax samples were derived from the National Center for Disease Control in Tbilisi representing the distribution of isolates recovered from the environment. This database was used to derive ecological niche models of Bacillus anthracis for the country (Figure 1). A second database was derived from livestock sampling efforts of the Laboratory of the Ministry of Agriculture. This database was mapped as positive and negative, based on bacteriology lab results. A third GIS layer was derived from the geographic locations of genotyped isolates published by Merabishvili et al. (2006). All geographic locations were mapped to the village level using the National Geospatial Intelligence Agency’s GeoNet Names Server (http://earth-info.nga.mil/gns/html/). Environmental Variables 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 1 km was utilized for this study. Environmental variables were prepared for ecological niche modeling in ArcView 3.3 using the GARP Clip Datasets extension and ArcGIS 9.3.1. Modeling Scenarios and Ecological Niche Modeling For this study, one modeling scenario was employed to examine the current geographic distribution of B. anthracis. The scenario contained eight environmental variables that described temperature, precipitation, vegetation indices, and elevation to create a model of the potential current distribution of B. anthracis (Table 1). The GARP ecological niche modeling tool was used to construct all models. 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. Those rules are then projected onto the landscape to predict the potential distribution of the species on Georgia. All models were produced in DesktopGARP version 1.1.3 and maps were generated using ArcGIS 9.3.1. We employed the best model subset routine, using and omission criteria of 10% and commission of 50% to select the best 10 models from a 200 model experimental design. Livestock isolates were plotted over the ecological niche model predictions to evaluate the most recent bacterial isolates against the predicted Bacillus anthracis habitat in Georgia. Figure 3. The distribution of bacterial isolates tested between 2009 – 2010 in Georgia. Red dots represent the locations of positive Bacillus anthracis recoveries from livestock. Note that isolates were recovered across those locations predicted as suitable B. anthracis habitat by the ecological niche modeling experiment. 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.