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Modeling the spatial distribution of anthrax in southern Kenya

BACKGROUND: Anthrax is an important zoonotic disease in Kenya associated with high animal and public health burden and widespread socio-economic impacts. The disease occurs in sporadic outbreaks that involve livestock, wildlife, and humans, but knowledge on factors that affect the geographic distrib...

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Autores principales: Otieno, Fredrick Tom, Gachohi, John, Gikuma-Njuru, Peter, Kariuki, Patrick, Oyas, Harry, Canfield, Samuel A., Blackburn, Jason K., Njenga, M. Kariuki, Bett, Bernard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8032196/
https://www.ncbi.nlm.nih.gov/pubmed/33780459
http://dx.doi.org/10.1371/journal.pntd.0009301
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author Otieno, Fredrick Tom
Gachohi, John
Gikuma-Njuru, Peter
Kariuki, Patrick
Oyas, Harry
Canfield, Samuel A.
Blackburn, Jason K.
Njenga, M. Kariuki
Bett, Bernard
author_facet Otieno, Fredrick Tom
Gachohi, John
Gikuma-Njuru, Peter
Kariuki, Patrick
Oyas, Harry
Canfield, Samuel A.
Blackburn, Jason K.
Njenga, M. Kariuki
Bett, Bernard
author_sort Otieno, Fredrick Tom
collection PubMed
description BACKGROUND: Anthrax is an important zoonotic disease in Kenya associated with high animal and public health burden and widespread socio-economic impacts. The disease occurs in sporadic outbreaks that involve livestock, wildlife, and humans, but knowledge on factors that affect the geographic distribution of these outbreaks is limited, challenging public health intervention planning. METHODS: Anthrax surveillance data reported in southern Kenya from 2011 to 2017 were modeled using a boosted regression trees (BRT) framework. An ensemble of 100 BRT experiments was developed using a variable set of 18 environmental covariates and 69 unique anthrax locations. Model performance was evaluated using AUC (area under the curve) ROC (receiver operating characteristics) curves. RESULTS: Cattle density, rainfall of wettest month, soil clay content, soil pH, soil organic carbon, length of longest dry season, vegetation index, temperature seasonality, in order, were identified as key variables for predicting environmental suitability for anthrax in the region. BRTs performed well with a mean AUC of 0.8. Areas highly suitable for anthrax were predicted predominantly in the southwestern region around the shared Kenya-Tanzania border and a belt through the regions and highlands in central Kenya. These suitable regions extend westwards to cover large areas in western highlands and the western regions around Lake Victoria and bordering Uganda. The entire eastern and lower-eastern regions towards the coastal region were predicted to have lower suitability for anthrax. CONCLUSION: These modeling efforts identified areas of anthrax suitability across southern Kenya, including high and medium agricultural potential regions and wildlife parks, important for tourism and foreign exchange. These predictions are useful for policy makers in designing targeted surveillance and/or control interventions in Kenya. We thank the staff of Directorate of Veterinary Services under the Ministry of Agriculture, Livestock and Fisheries, for collecting and providing the anthrax historical occurrence data.
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spelling pubmed-80321962021-04-15 Modeling the spatial distribution of anthrax in southern Kenya Otieno, Fredrick Tom Gachohi, John Gikuma-Njuru, Peter Kariuki, Patrick Oyas, Harry Canfield, Samuel A. Blackburn, Jason K. Njenga, M. Kariuki Bett, Bernard PLoS Negl Trop Dis Research Article BACKGROUND: Anthrax is an important zoonotic disease in Kenya associated with high animal and public health burden and widespread socio-economic impacts. The disease occurs in sporadic outbreaks that involve livestock, wildlife, and humans, but knowledge on factors that affect the geographic distribution of these outbreaks is limited, challenging public health intervention planning. METHODS: Anthrax surveillance data reported in southern Kenya from 2011 to 2017 were modeled using a boosted regression trees (BRT) framework. An ensemble of 100 BRT experiments was developed using a variable set of 18 environmental covariates and 69 unique anthrax locations. Model performance was evaluated using AUC (area under the curve) ROC (receiver operating characteristics) curves. RESULTS: Cattle density, rainfall of wettest month, soil clay content, soil pH, soil organic carbon, length of longest dry season, vegetation index, temperature seasonality, in order, were identified as key variables for predicting environmental suitability for anthrax in the region. BRTs performed well with a mean AUC of 0.8. Areas highly suitable for anthrax were predicted predominantly in the southwestern region around the shared Kenya-Tanzania border and a belt through the regions and highlands in central Kenya. These suitable regions extend westwards to cover large areas in western highlands and the western regions around Lake Victoria and bordering Uganda. The entire eastern and lower-eastern regions towards the coastal region were predicted to have lower suitability for anthrax. CONCLUSION: These modeling efforts identified areas of anthrax suitability across southern Kenya, including high and medium agricultural potential regions and wildlife parks, important for tourism and foreign exchange. These predictions are useful for policy makers in designing targeted surveillance and/or control interventions in Kenya. We thank the staff of Directorate of Veterinary Services under the Ministry of Agriculture, Livestock and Fisheries, for collecting and providing the anthrax historical occurrence data. Public Library of Science 2021-03-29 /pmc/articles/PMC8032196/ /pubmed/33780459 http://dx.doi.org/10.1371/journal.pntd.0009301 Text en https://creativecommons.org/publicdomain/zero/1.0/This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 (https://creativecommons.org/publicdomain/zero/1.0/) public domain dedication.
spellingShingle Research Article
Otieno, Fredrick Tom
Gachohi, John
Gikuma-Njuru, Peter
Kariuki, Patrick
Oyas, Harry
Canfield, Samuel A.
Blackburn, Jason K.
Njenga, M. Kariuki
Bett, Bernard
Modeling the spatial distribution of anthrax in southern Kenya
title Modeling the spatial distribution of anthrax in southern Kenya
title_full Modeling the spatial distribution of anthrax in southern Kenya
title_fullStr Modeling the spatial distribution of anthrax in southern Kenya
title_full_unstemmed Modeling the spatial distribution of anthrax in southern Kenya
title_short Modeling the spatial distribution of anthrax in southern Kenya
title_sort modeling the spatial distribution of anthrax in southern kenya
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8032196/
https://www.ncbi.nlm.nih.gov/pubmed/33780459
http://dx.doi.org/10.1371/journal.pntd.0009301
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