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Culex pipiens distribution in Tunisia: Identification of suitable areas through Random Forest and MaxEnt approaches

BACKGROUND: Tunisia has experienced several West Nile virus (WNV) outbreaks since 1997. Yet, there is limited information on the spatial distribution of the main WNV mosquito vector Culex pipiens suitability at the national level. OBJECTIVES: In the present study, our aim was to predict and evaluate...

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Autores principales: Amdouni, Jihane, Conte, Annamaria, Ippoliti, Carla, Candeloro, Luca, Tora, Susanna, Sghaier, Soufien, Hassine, Thameur Ben, Fakhfekh, Emna Ayari, Savini, Giovanni, Hammami, Salah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9677390/
https://www.ncbi.nlm.nih.gov/pubmed/36005907
http://dx.doi.org/10.1002/vms3.897
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author Amdouni, Jihane
Conte, Annamaria
Ippoliti, Carla
Candeloro, Luca
Tora, Susanna
Sghaier, Soufien
Hassine, Thameur Ben
Fakhfekh, Emna Ayari
Savini, Giovanni
Hammami, Salah
author_facet Amdouni, Jihane
Conte, Annamaria
Ippoliti, Carla
Candeloro, Luca
Tora, Susanna
Sghaier, Soufien
Hassine, Thameur Ben
Fakhfekh, Emna Ayari
Savini, Giovanni
Hammami, Salah
author_sort Amdouni, Jihane
collection PubMed
description BACKGROUND: Tunisia has experienced several West Nile virus (WNV) outbreaks since 1997. Yet, there is limited information on the spatial distribution of the main WNV mosquito vector Culex pipiens suitability at the national level. OBJECTIVES: In the present study, our aim was to predict and evaluate the potential and current distribution of Cx. pipiens in Tunisia. METHODS: To this end, two species distribution models were used, i.e. MaxEnt and Random Forest. Occurrence records for Cx. pipiens were obtained from adult and larvae sampled in Tunisia from 2014 to 2017. Climatic and human factors were used as predictors to model the Cx. pipiens geographical distribution. Mean decrease accuracy and mean decrease Gini indices were calculated to evaluate the importance of the impact of different environmental and human variables on the probability distribution of Cx. pipiens. RESULTS: Suitable habitats were mainly distributed next to oases, in the north and eastern part of the country. The most important predictor was the population density in both models. The study found out that the governorates of Monastir, Nabeul, Manouba, Ariana, Bizerte, Gabes, Medenine and Kairouan are at highest epidemic risk. CONCLUSIONS: The potential distribution of Cx. pipiens coincides geographically with the observed distribution of the disease in humans in Tunisia. Our study has the potential for driving control effort in the fight against West Nile vector in Tunisia.
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spelling pubmed-96773902022-11-22 Culex pipiens distribution in Tunisia: Identification of suitable areas through Random Forest and MaxEnt approaches Amdouni, Jihane Conte, Annamaria Ippoliti, Carla Candeloro, Luca Tora, Susanna Sghaier, Soufien Hassine, Thameur Ben Fakhfekh, Emna Ayari Savini, Giovanni Hammami, Salah Vet Med Sci OTHER BACKGROUND: Tunisia has experienced several West Nile virus (WNV) outbreaks since 1997. Yet, there is limited information on the spatial distribution of the main WNV mosquito vector Culex pipiens suitability at the national level. OBJECTIVES: In the present study, our aim was to predict and evaluate the potential and current distribution of Cx. pipiens in Tunisia. METHODS: To this end, two species distribution models were used, i.e. MaxEnt and Random Forest. Occurrence records for Cx. pipiens were obtained from adult and larvae sampled in Tunisia from 2014 to 2017. Climatic and human factors were used as predictors to model the Cx. pipiens geographical distribution. Mean decrease accuracy and mean decrease Gini indices were calculated to evaluate the importance of the impact of different environmental and human variables on the probability distribution of Cx. pipiens. RESULTS: Suitable habitats were mainly distributed next to oases, in the north and eastern part of the country. The most important predictor was the population density in both models. The study found out that the governorates of Monastir, Nabeul, Manouba, Ariana, Bizerte, Gabes, Medenine and Kairouan are at highest epidemic risk. CONCLUSIONS: The potential distribution of Cx. pipiens coincides geographically with the observed distribution of the disease in humans in Tunisia. Our study has the potential for driving control effort in the fight against West Nile vector in Tunisia. John Wiley and Sons Inc. 2022-08-25 /pmc/articles/PMC9677390/ /pubmed/36005907 http://dx.doi.org/10.1002/vms3.897 Text en © 2022 The Authors. Veterinary Medicine and Science published by John Wiley & Sons Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle OTHER
Amdouni, Jihane
Conte, Annamaria
Ippoliti, Carla
Candeloro, Luca
Tora, Susanna
Sghaier, Soufien
Hassine, Thameur Ben
Fakhfekh, Emna Ayari
Savini, Giovanni
Hammami, Salah
Culex pipiens distribution in Tunisia: Identification of suitable areas through Random Forest and MaxEnt approaches
title Culex pipiens distribution in Tunisia: Identification of suitable areas through Random Forest and MaxEnt approaches
title_full Culex pipiens distribution in Tunisia: Identification of suitable areas through Random Forest and MaxEnt approaches
title_fullStr Culex pipiens distribution in Tunisia: Identification of suitable areas through Random Forest and MaxEnt approaches
title_full_unstemmed Culex pipiens distribution in Tunisia: Identification of suitable areas through Random Forest and MaxEnt approaches
title_short Culex pipiens distribution in Tunisia: Identification of suitable areas through Random Forest and MaxEnt approaches
title_sort culex pipiens distribution in tunisia: identification of suitable areas through random forest and maxent approaches
topic OTHER
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9677390/
https://www.ncbi.nlm.nih.gov/pubmed/36005907
http://dx.doi.org/10.1002/vms3.897
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