Literature DB >> 28188765

Predicting the potential distribution of main malaria vectors Anopheles stephensi, An. culicifacies s.l. and An. fluviatilis s.l. in Iran based on maximum entropy model.

Kamran Pakdad1, Ahmad Ali Hanafi-Bojd2, Hassan Vatandoost3, Mohammad Mehdi Sedaghat4, Ahmad Raeisi5, Abdolreza Salahi Moghaddam6, Abbas Rahimi Foroushani7.   

Abstract

Malaria is considered as a major public health problem in southern areas of Iran. The goal of this study was to predict best ecological niches of three main malaria vectors of Iran: Anopheles stephensi, Anopheles culicifacies s.l. and Anopheles fluviatilis s.l. A databank was created which included all published data about Anopheles species of Iran from 1961 to 2015. The suitable environmental niches for the three above mentioned Anopheles species were predicted using maximum entropy model (MaxEnt). AUC (area under Roc curve) values were 0.943, 0.974 and 0.956 for An. stephensi, An. culicifacies s.l. and An. fluviatilis s.l respectively, which are considered as high potential power of model in the prediction of species niches. The biggest bioclimatic contributor for An. stephensi and An. fluviatilis s.l. was bio 15 (precipitation seasonality), 25.5% and 36.1% respectively, followed by bio 1 (annual mean temperature), 20.8% for An. stephensi and bio 4 (temperature seasonality) with 49.4% contribution for An. culicifacies s.l. This is the first step in the mapping of the country's malaria vectors. Hence, future weather situation can change the dispersal maps of Anopheles. Iran is under elimination phase of malaria, so that such spatio-temporal studies are essential and could provide guideline for decision makers for IVM strategies in problematic areas.
Copyright © 2017 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Anopheles culicifacies s.l; Anopheles fluviatilis s.l; Anopheles stephensi; Iran; Malaria; Modeling

Mesh:

Year:  2017        PMID: 28188765     DOI: 10.1016/j.actatropica.2017.02.004

Source DB:  PubMed          Journal:  Acta Trop        ISSN: 0001-706X            Impact factor:   3.112


  6 in total

1.  Spatio-temporal Prediction of the Malaria Transmission Risk in Minab District (Hormozgan Province, Southern Iran).

Authors:  Abdolreza Salahi-Moghaddam; Habibollah Turki; Masoud Yeryan; Màrius V Fuentes
Journal:  Acta Parasitol       Date:  2022-08-11       Impact factor: 1.534

2.  Predicting environmentally suitable areas for Anopheles superpictus Grassi (s.l.), Anopheles maculipennis Meigen (s.l.) and Anopheles sacharovi Favre (Diptera: Culicidae) in Iran.

Authors:  Ahmad Ali Hanafi-Bojd; Mohammad Mehdi Sedaghat; Hassan Vatandoost; Shahyad Azari-Hamidian; Kamran Pakdad
Journal:  Parasit Vectors       Date:  2018-07-03       Impact factor: 3.876

3.  Using Ecological Niche Modeling to Predict the Spatial Distribution of Anopheles maculipennis s.l. and Culex theileri (Diptera: Culicidae) in Central Iran.

Authors:  Najmeh Hesami; Mohammad Reza Abai; Hassan Vatandoost; Mostafa Alizadeh; Mahboubeh Fatemi; Javad Ramazanpour; Ahmad Ali Hanafi-Bojd
Journal:  J Arthropod Borne Dis       Date:  2019-06-24       Impact factor: 1.198

4.  Modeling the Potential Distribution of the Malaria Vector Anopheles (Ano.) pseudopunctipennis Theobald (Diptera: Culicidae) in Arid Regions of Northern Chile.

Authors:  Lara Valderrama; Salvador Ayala; Carolina Reyes; Christian R González
Journal:  Front Public Health       Date:  2021-05-11

5.  Little pigeons can carry great messages: potential distribution and ecology of Uranotaenia (Pseudoficalbia) unguiculata Edwards, 1913 (Diptera: Culicidae), a lesser-known mosquito species from the Western Palaearctic.

Authors:  Serhii Filatov
Journal:  Parasit Vectors       Date:  2017-10-10       Impact factor: 3.876

6.  Projecting potential spatial and temporal changes in the distribution of Plasmodium vivax and Plasmodium falciparum malaria in China with climate change.

Authors:  Samuel Hundessa; Gail Williams; Shanshan Li; De Li Liu; Wei Cao; Hongyan Ren; Jinpeng Guo; Antonio Gasparrini; Kristie Ebi; Wenyi Zhang; Yuming Guo
Journal:  Sci Total Environ       Date:  2018-02-07       Impact factor: 7.963

  6 in total

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