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Ecological Niche Modeling for the Prediction of the Geographic Distribution of Cutaneous Leishmaniasis in Tunisia
Cutaneous leishmaniasis is a very complex disease involving multiple factors that limit its emergence and spatial distribution. Prediction of cutaneous leishmaniasis epidemics in Tunisia remains difficult because most of the epidemiological tools used so far are descriptive in nature and mainly focu...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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The American Society of Tropical Medicine and Hygiene
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4824228/ https://www.ncbi.nlm.nih.gov/pubmed/26856914 http://dx.doi.org/10.4269/ajtmh.15-0345 |
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author | Chalghaf, Bilel Chlif, Sadok Mayala, Benjamin Ghawar, Wissem Bettaieb, Jihène Harrabi, Myriam Benie, Goze Bertin Michael, Edwin Salah, Afif Ben |
author_facet | Chalghaf, Bilel Chlif, Sadok Mayala, Benjamin Ghawar, Wissem Bettaieb, Jihène Harrabi, Myriam Benie, Goze Bertin Michael, Edwin Salah, Afif Ben |
author_sort | Chalghaf, Bilel |
collection | PubMed |
description | Cutaneous leishmaniasis is a very complex disease involving multiple factors that limit its emergence and spatial distribution. Prediction of cutaneous leishmaniasis epidemics in Tunisia remains difficult because most of the epidemiological tools used so far are descriptive in nature and mainly focus on a time dimension. The purpose of this work is to predict the potential geographic distribution of Phlebotomus papatasi and zoonotic cutaneous leishmaniasis caused by Leishmania major in Tunisia using Grinnellian ecological niche modeling. We attempted to assess the importance of environmental factors influencing the potential distribution of P. papatasi and cutaneous leishmaniasis caused by L. major. Vectors were trapped in central Tunisia during the transmission season using CDC light traps (John W. Hock Co., Gainesville, FL). A global positioning system was used to record the geographical coordinates of vector occurrence points and households tested positive for cutaneous leishmaniasis caused by L. major. Nine environmental layers were used as predictor variables to model the P. papatasi geographical distribution and five variables were used to model the L. major potential distribution. Ecological niche modeling was used to relate known species' occurrence points to values of environmental factors for these same points to predict the presence of the species in unsampled regions based on the value of the predictor variables. Rainfall and temperature contributed the most as predictors for sand flies and human case distributions. Ecological niche modeling anticipated the current distribution of P. papatasi with the highest suitability for species occurrence in the central and southeastern part of Tunisian. Furthermore, our study demonstrated that governorates of Gafsa, Sidi Bouzid, and Kairouan are at highest epidemic risk. |
format | Online Article Text |
id | pubmed-4824228 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | The American Society of Tropical Medicine and Hygiene |
record_format | MEDLINE/PubMed |
spelling | pubmed-48242282016-04-19 Ecological Niche Modeling for the Prediction of the Geographic Distribution of Cutaneous Leishmaniasis in Tunisia Chalghaf, Bilel Chlif, Sadok Mayala, Benjamin Ghawar, Wissem Bettaieb, Jihène Harrabi, Myriam Benie, Goze Bertin Michael, Edwin Salah, Afif Ben Am J Trop Med Hyg Articles Cutaneous leishmaniasis is a very complex disease involving multiple factors that limit its emergence and spatial distribution. Prediction of cutaneous leishmaniasis epidemics in Tunisia remains difficult because most of the epidemiological tools used so far are descriptive in nature and mainly focus on a time dimension. The purpose of this work is to predict the potential geographic distribution of Phlebotomus papatasi and zoonotic cutaneous leishmaniasis caused by Leishmania major in Tunisia using Grinnellian ecological niche modeling. We attempted to assess the importance of environmental factors influencing the potential distribution of P. papatasi and cutaneous leishmaniasis caused by L. major. Vectors were trapped in central Tunisia during the transmission season using CDC light traps (John W. Hock Co., Gainesville, FL). A global positioning system was used to record the geographical coordinates of vector occurrence points and households tested positive for cutaneous leishmaniasis caused by L. major. Nine environmental layers were used as predictor variables to model the P. papatasi geographical distribution and five variables were used to model the L. major potential distribution. Ecological niche modeling was used to relate known species' occurrence points to values of environmental factors for these same points to predict the presence of the species in unsampled regions based on the value of the predictor variables. Rainfall and temperature contributed the most as predictors for sand flies and human case distributions. Ecological niche modeling anticipated the current distribution of P. papatasi with the highest suitability for species occurrence in the central and southeastern part of Tunisian. Furthermore, our study demonstrated that governorates of Gafsa, Sidi Bouzid, and Kairouan are at highest epidemic risk. The American Society of Tropical Medicine and Hygiene 2016-04-06 /pmc/articles/PMC4824228/ /pubmed/26856914 http://dx.doi.org/10.4269/ajtmh.15-0345 Text en ©The American Society of Tropical Medicine and Hygiene This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Articles Chalghaf, Bilel Chlif, Sadok Mayala, Benjamin Ghawar, Wissem Bettaieb, Jihène Harrabi, Myriam Benie, Goze Bertin Michael, Edwin Salah, Afif Ben Ecological Niche Modeling for the Prediction of the Geographic Distribution of Cutaneous Leishmaniasis in Tunisia |
title | Ecological Niche Modeling for the Prediction of the Geographic Distribution of Cutaneous Leishmaniasis in Tunisia |
title_full | Ecological Niche Modeling for the Prediction of the Geographic Distribution of Cutaneous Leishmaniasis in Tunisia |
title_fullStr | Ecological Niche Modeling for the Prediction of the Geographic Distribution of Cutaneous Leishmaniasis in Tunisia |
title_full_unstemmed | Ecological Niche Modeling for the Prediction of the Geographic Distribution of Cutaneous Leishmaniasis in Tunisia |
title_short | Ecological Niche Modeling for the Prediction of the Geographic Distribution of Cutaneous Leishmaniasis in Tunisia |
title_sort | ecological niche modeling for the prediction of the geographic distribution of cutaneous leishmaniasis in tunisia |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4824228/ https://www.ncbi.nlm.nih.gov/pubmed/26856914 http://dx.doi.org/10.4269/ajtmh.15-0345 |
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