Assessing geographic and climatic variables to predict the potential distribution of the visceral leishmaniasis vector Lutzomyia longipalpis in the state of Espírito Santo, Brazil

Visceral leishmaniasis (VL) is an infectious disease caused by the protozoa Leishmania chagasi, whose main vector in South America is Lutzomyia longipalpis. The disease was diagnosed in the Brazilian state of Espírito Santo (ES) for the first time in 1968. Currently, this disease has been considered...

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Main Authors: Karina Bertazo Del Carro, Gustavo Rocha Leite, Amandio Gonçalves de Oliveira Filho, Claudiney Biral dos Santos, Israel de Souza Pinto, Blima Fux, Aloísio Falqueto, Abdallah M. Samy
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2020-01-01
Series:PLoS ONE
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7500671/?tool=EBI
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spelling doaj-bb9b62e074b6481b98b81669aa1aa59c2020-11-25T03:54:41ZengPublic Library of Science (PLoS)PLoS ONE1932-62032020-01-01159Assessing geographic and climatic variables to predict the potential distribution of the visceral leishmaniasis vector Lutzomyia longipalpis in the state of Espírito Santo, BrazilKarina Bertazo Del CarroGustavo Rocha LeiteAmandio Gonçalves de Oliveira FilhoClaudiney Biral dos SantosIsrael de Souza PintoBlima FuxAloísio FalquetoAbdallah M. SamyVisceral leishmaniasis (VL) is an infectious disease caused by the protozoa Leishmania chagasi, whose main vector in South America is Lutzomyia longipalpis. The disease was diagnosed in the Brazilian state of Espírito Santo (ES) for the first time in 1968. Currently, this disease has been considered endemic in 10 municipalities. Furthermore, the presence of L. longipalpis has been detected in eight other municipalities where the transmission has not been reported thus far. In this study, we performed species distribution modeling (SDM) to identify new and most likely receptive areas for VL transmission in ES. The sandflies were both actively and passively collected in various rural area of ES between 1986 and 2017. The collection points were georeferenced using a global positioning system device. Climatic data were retrieved from the WorldClim database, whereas geographic data were obtained from the National Institute for Space Research and the Integrated System of Geospatial Bases of the State of Espírito Santo. The maximum entropy algorithm was used through the MIAmaxent R package to train and test the distribution models for L. longipalpis. The major contributor to model generation was rocky outcrops, followed by temperature seasonality. The SDM predicted the expansion of the L. longipalpis-prone area in the Doce River Valley and limited the probability of expanding outside its watershed. Once the areas predicted suitable for L. longipalpis occurrence are determined, we can avoid the inefficient use of public resources in conducting canine serological surveys where the vector insect does not occur.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7500671/?tool=EBI
collection DOAJ
language English
format Article
sources DOAJ
author Karina Bertazo Del Carro
Gustavo Rocha Leite
Amandio Gonçalves de Oliveira Filho
Claudiney Biral dos Santos
Israel de Souza Pinto
Blima Fux
Aloísio Falqueto
Abdallah M. Samy
spellingShingle Karina Bertazo Del Carro
Gustavo Rocha Leite
Amandio Gonçalves de Oliveira Filho
Claudiney Biral dos Santos
Israel de Souza Pinto
Blima Fux
Aloísio Falqueto
Abdallah M. Samy
Assessing geographic and climatic variables to predict the potential distribution of the visceral leishmaniasis vector Lutzomyia longipalpis in the state of Espírito Santo, Brazil
PLoS ONE
author_facet Karina Bertazo Del Carro
Gustavo Rocha Leite
Amandio Gonçalves de Oliveira Filho
Claudiney Biral dos Santos
Israel de Souza Pinto
Blima Fux
Aloísio Falqueto
Abdallah M. Samy
author_sort Karina Bertazo Del Carro
title Assessing geographic and climatic variables to predict the potential distribution of the visceral leishmaniasis vector Lutzomyia longipalpis in the state of Espírito Santo, Brazil
title_short Assessing geographic and climatic variables to predict the potential distribution of the visceral leishmaniasis vector Lutzomyia longipalpis in the state of Espírito Santo, Brazil
title_full Assessing geographic and climatic variables to predict the potential distribution of the visceral leishmaniasis vector Lutzomyia longipalpis in the state of Espírito Santo, Brazil
title_fullStr Assessing geographic and climatic variables to predict the potential distribution of the visceral leishmaniasis vector Lutzomyia longipalpis in the state of Espírito Santo, Brazil
title_full_unstemmed Assessing geographic and climatic variables to predict the potential distribution of the visceral leishmaniasis vector Lutzomyia longipalpis in the state of Espírito Santo, Brazil
title_sort assessing geographic and climatic variables to predict the potential distribution of the visceral leishmaniasis vector lutzomyia longipalpis in the state of espírito santo, brazil
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2020-01-01
description Visceral leishmaniasis (VL) is an infectious disease caused by the protozoa Leishmania chagasi, whose main vector in South America is Lutzomyia longipalpis. The disease was diagnosed in the Brazilian state of Espírito Santo (ES) for the first time in 1968. Currently, this disease has been considered endemic in 10 municipalities. Furthermore, the presence of L. longipalpis has been detected in eight other municipalities where the transmission has not been reported thus far. In this study, we performed species distribution modeling (SDM) to identify new and most likely receptive areas for VL transmission in ES. The sandflies were both actively and passively collected in various rural area of ES between 1986 and 2017. The collection points were georeferenced using a global positioning system device. Climatic data were retrieved from the WorldClim database, whereas geographic data were obtained from the National Institute for Space Research and the Integrated System of Geospatial Bases of the State of Espírito Santo. The maximum entropy algorithm was used through the MIAmaxent R package to train and test the distribution models for L. longipalpis. The major contributor to model generation was rocky outcrops, followed by temperature seasonality. The SDM predicted the expansion of the L. longipalpis-prone area in the Doce River Valley and limited the probability of expanding outside its watershed. Once the areas predicted suitable for L. longipalpis occurrence are determined, we can avoid the inefficient use of public resources in conducting canine serological surveys where the vector insect does not occur.
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7500671/?tool=EBI
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