Ecological Niche Modelling of the Bacillus anthracis A1.a sub-lineage in Kazakhstan

Mullins, J. Thomas; Lukhnova, Larissa; Aikimbayev, Alim; Pazilov, Yerlan; Ert, Matthew Van; Blackburn, Jason K.

doi:10.1186/1472-6785-11-32

Cited by 43 publications

(67 citation statements)

References 42 publications

(82 reference statements)

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“…Predictive ecological niche modeling (ENM) is increasingly being used to predict the geographic distribution of multiple zoonotic pathogens and parasites, and to investigate and identify important factors associated with outbreak/case locations (Adjemian et al 2006;Blackburn et al 2007;Williams and Peterson 2009), and related ecological conditions (Blackburn 2006;Rogers 2006;Joyner 2010;Mullins et al 2011). ENM is the process of identifying non-random relationships between known species occurrence locations (here the pathogen/parasite, host, reservoir, or vector), and climatic or environmental variables derived from interpolated FIG.…”

Section: Ecological Niche Modelingmentioning

confidence: 99%

“…The relationships derived are then applied to the landscape (in a global information system [GIS]), to ''project'' those relationships onto the geography of the area of interest in the form of binary presence/absence (in GARP), or cumulative probabilities of presence (in MaxEnt). Recently, GARP rules have been used to build and graph climatic envelopes for a specific genetic sub-lineage of Bacillus anthracis in Kazakhstan, compared to models built from larger data sets representing outbreaks regardless of genotype (Mullins et al 2011). This process also allows the user to project models onto landscapes where occurrence data are unavailable, such as when surveillance or reporting are lacking (Blackburn 2010), or onto the same landscape in future time periods to evaluate the effects of climate change (Holt et al 2009;Blackburn 2010;Joyner et al 2010).…”

mentioning

confidence: 99%

“…While the transmission dynamics of anthrax are not fully understood, there is some consensus on the environmental conditions that support B. anthracis spore survival (HughJones and Blackburn 2009). ENM has been employed to map the potential distribution of B. anthracis in the contiguous United States (Blackburn et al 2007), and Kazakhstan Mullins et al 2011). As an obligate spillover pathogen, these modeling efforts arguably delineate the geographic space associated with spillover, where the pathogen occurs and anthrax epidemics are likely to occur and recur (Blackburn 2010).…”

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confidence: 99%

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Modeling of Wildlife-Associated Zoonoses: Applications and Caveats

Alexander

Lewis

Marathe

et al. 2012

Vector-Borne and Zoonotic Diseases

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Wildlife species are identified as an important source of emerging zoonotic disease. Accordingly, public health programs have attempted to expand in scope to include a greater focus on wildlife and its role in zoonotic disease outbreaks. Zoonotic disease transmission dynamics involving wildlife are complex and nonlinear, presenting a number of challenges. First, empirical characterization of wildlife host species and pathogen systems are often lacking, and insight into one system may have little application to another involving the same host species and pathogen. Pathogen transmission characterization is difficult due to the changing nature of population size and density associated with wildlife hosts. Infectious disease itself may influence wildlife population demographics through compensatory responses that may evolve, such as decreased age to reproduction. Furthermore, wildlife reservoir dynamics can be complex, involving various host species and populations that may vary in their contribution to pathogen transmission and persistence over space and time. Mathematical models can provide an important tool to engage these complex systems, and there is an urgent need for increased computational focus on the coupled dynamics that underlie pathogen spillover at the humanwildlife interface. Often, however, scientists conducting empirical studies on emerging zoonotic disease do not have the necessary skill base to choose, develop, and apply models to evaluate these complex systems. How do modeling frameworks differ and what considerations are important when applying modeling tools to the study of zoonotic disease? Using zoonotic disease examples, we provide an overview of several common approaches and general considerations important in the modeling of wildlife-associated zoonoses.

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Section: Ecological Niche Modelingmentioning

confidence: 99%

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confidence: 99%

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Modeling of Wildlife-Associated Zoonoses: Applications and Caveats

Alexander

Lewis

Marathe

et al. 2012

Vector-Borne and Zoonotic Diseases

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“…For examples of the relationship between rule sets and geographic predictions, see Mullins and others. 17,18 Environmental data. Environmental variables used in this study followed Mullins and others 17 from a study of neighboring Kazakhstan to allow for comparison.…”

Section: Methodsmentioning

confidence: 99%

“…Because of the iterative nature of GARP, the ruleset approach does not arrive at a single solution. 18 Because of this, model performance can be affected by variation in input data. 22 We developed 10 separate GARP experiments to evaluate the effect of input variability on model output.…”

Section: Methodsmentioning

confidence: 99%

Modeling the Ecological Niche of Bacillus anthracis to Map Anthrax Risk in Kyrgyzstan

Blackburn¹,

Matakarimov²,

Kozhokeeva³

et al. 2017

Am J Trop Med Hyg

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Abstract. Anthrax, caused by the environmental bacterium Bacillus anthracis, is an important zoonosis nearly worldwide. In Central Asia, anthrax represents a major veterinary and public health concern. In the Republic of Kyrgyzstan, ongoing anthrax outbreaks have been reported in humans associated with handling infected livestock and contaminated animal by-products such as meat or hides. The current anthrax situation has prompted calls for improved insights into the epidemiology, ecology, and spatial distribution of the disease in Kyrgyzstan to better inform control and surveillance. Disease control for both humans and livestock relies on annual livestock vaccination ahead of outbreaks. Toward this, we used a historic database of livestock anthrax reported from 1932 to 2006 mapped at high resolution to develop an ecological niche model-based prediction of B. anthracis across Kyrgyzstan and identified spatial clusters of livestock anthrax using a cluster morphology statistic. We also defined the seasonality of outbreaks in livestock. Cattle were the most frequently reported across the time period, with the greatest number of cases in late summer months. Our niche models defined four areas as suitable to support pathogen persistence, the plateaus near Talas and Bishkek, the valleys of western Kyrgyzstan along the Fergana Valley, and the low-lying areas along the shore of Lake Isyk-Kul. These areas should be considered "at risk" for livestock anthrax and subsequent human cases. Areas defined by the niche models can be used to prioritize anthrax surveillance and inform efforts to target livestock vaccination campaigns.

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Elk resource selection and implications for anthrax management in Montana

Morris

Proffitt

Asher³

et al. 2015

J Wildl Manag

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Anthrax, caused by the spore-forming bacterium Bacillus anthracis, is a zoonotic disease that affects humans and animals throughout the world. In North America, anthrax outbreaks occur in livestock and wildlife species. Vaccine administration in wildlife is untenable; the most effective form of management is surveillance and decontamination of carcasses. Successful management is critical because untreated carcasses can create infectious zones increasing risk for other susceptible hosts. We studied the bacterium in a re-emerging anthrax zone in southwest Montana. In 2008, a large anthraxepizootic primarily affected a domestic bison (Bison bison) herd and the male segment of a free-ranging elk (Cervus elaphus) herd in southwestern Montana. Following the outbreak, we initiated a telemetry study on elk to evaluate resource selection during the anthrax season to assist with anthrax management. We used a mixed effects generalized linear model (GLM) to estimate resource selection by male elk, and we mapped habitat preferences across the landscape. We overlaid preferred habitats on ecological niche model-based estimates of B. anthracis presence. We observed significant overlap between areas with a high predicted probability of male elk selection and B. anthracis potential. These potentially risky areas of elk and B. anthracis overlap were broadly spread over public and private lands. Future outbreaks in the region are probable, and this analysis identified the spatial extent of the risk area in the region, which can be used to prioritize anthrax surveillance.

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Ecological Niche Modelling of the Bacillus anthracis A1.a sub-lineage in Kazakhstan

Cited by 43 publications

References 42 publications

Modeling of Wildlife-Associated Zoonoses: Applications and Caveats

Modeling of Wildlife-Associated Zoonoses: Applications and Caveats

Modeling the Ecological Niche of Bacillus anthracis to Map Anthrax Risk in Kyrgyzstan

Elk resource selection and implications for anthrax management in Montana

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