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Title
Ecological niche modeling as a tool for prediction of the potential geographic distribution of Bacillus anthracis spores in Tanzania
Author(s)
Mwakapeje, E. R.;Ndimuligo, S. A.;Mosomtai, G.;Ayebare, S.;Nyakarahuka, L.;Nonga, H. E.;Mdegela, R. H.;Skjerve, E.
Published
2019
Publisher
International Journal of Infectious Diseases
Published Version DOI
https://doi.org/10.1016/j.ijid.2018.11.367
Abstract
Introduction: Anthrax is caused by the spore-forming, Gram-positive bacterium Bacillus anthracis. The aim of this study was to predict the potential distribution of B. anthracis in Tanzania and produce epidemiological evidence for the management of anthrax outbreaks in the country. Methods: The Maxent algorithm was used to predict areas at risk of anthrax outbreaks based on the occurrence and environmental data in Arusha and Kilimanjaro regions; the model was later transferred to predict the entire country. Seventy percent of the occurrence data were used to train the model, while 30% were used for model evaluation. Results: Four regions of northern Tanzania are predicted to have a high risk for anthrax outbreaks, while the southern and western regions had low-risk areas. Soil type (56.5%), soil pH (23.7%), and isothermally (10.4%) were the most important variables for the model prediction, and the most significant soil types were solonetz, fluvisols, and lithosols. Conclusions: A strong risk level across districts of the Tanzania mainland was identified in this study. A total of 18 districts in Tanzania Mainland are predicted to be at very high risk of an anthrax outbreak occurrence. These findings are important for policymakers to effectively mount targeted control measures for anthrax outbreaks in Tanzania. (c) 2018 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords
Habitat suitability;Bacillus anthracis;Spatial distribution;Ecological niche modeling;Tanzania;one health;epidemiology;management;outbreaks;Infectious Diseases
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PUB24416