Literature DB >> 7485706

Predictions of adult Anopheles albimanus densities in villages based on distances to remotely sensed larval habitats.

E Rejmankova1, D R Roberts, A Pawley, S Manguin, J Polanco.   

Abstract

Remote sensing is particularly helpful for assessing the location and extent of vegetation formations, such as herbaceous wetlands, that are difficult to examine on the ground. Marshes that are sparsely populated with emergent macrophytes and dense cyanobacterial mats have previously been identified as very productive Anopheles albimanus larval habitats. This type of habitat was detectable on a classified multispectral System Probatoire d'Observation de la Terre image of northern Belize as a mixture of two isoclasses. A similar spectral signature is characteristic for vegetation of river margins consisting of aquatic grasses and water hyacinth, which constitutes another productive larval habitat. Based on the distance between human settlements (sites) of various sizes and the nearest marsh/river exhibiting this particular class combination, we selected two groups of sites: those located closer than 500 m and those located more than 1,500 m from such habitats. Based on previous adult collections near larval habitats, we defined a landing rate of 0.5 mosquitoes/human/min from 6:30 PM to 8:00 PM as the threshold for high (> or = 0.5 mosquitoes/human/min) versus low (< 0.5 mosquitoes/human/min) densities of An. albimanus. Sites located less than 500 m from the habitat were predicted as having values higher than this threshold, while lower values were predicted for sites located greater than 1,500 m from the habitat. Predictions were verified by collections of mosquitoes landing on humans. The predictions were 100% accurate for sites in the > 1,500-m category and 89% accurate for sites in the < 500-m category.

Entities:  

Keywords:  NASA Discipline General Space Life Sciences; Non-NASA Center

Mesh:

Year:  1995        PMID: 7485706     DOI: 10.4269/ajtmh.1995.53.482

Source DB:  PubMed          Journal:  Am J Trop Med Hyg        ISSN: 0002-9637            Impact factor:   2.345


  20 in total

1.  Evaluation of environmental data for identification of Anopheles (Diptera: Culicidae) aquatic larval habitats in Kisumu and Malindi, Kenya.

Authors:  Benjamin G Jacob; Kristopher L Arheart; Daniel A Griffith; Charles M Mbogo; Andrew K Githeko; James L Regens; John I Githure; Robert Novak; John C Beier
Journal:  J Med Entomol       Date:  2005-09       Impact factor: 2.278

2.  Disentangling the effect of local and global spatial variation on a mosquito-borne infection in a neotropical heterogeneous environment.

Authors:  María-Eugenia Grillet; Roberto Barrera; Juan-Eudes Martínez; Jesús Berti; Marie-Josée Fortin
Journal:  Am J Trop Med Hyg       Date:  2010-02       Impact factor: 2.345

3.  Developing GIS-based eastern equine encephalitis vector-host models in Tuskegee, Alabama.

Authors:  Benjamin G Jacob; Nathan D Burkett-Cadena; Jeffrey C Luvall; Sarah H Parcak; Christopher J W McClure; Laura K Estep; Geoffrey E Hill; Eddie W Cupp; Robert J Novak; Thomas R Unnasch
Journal:  Int J Health Geogr       Date:  2010-02-24       Impact factor: 3.918

4.  Mapping the ranges and relative abundance of the two principal African malaria vectors, Anopheles gambiae sensu stricto and An. arabiensis, using climate data.

Authors:  S W Lindsay; L Parson; C J Thomas
Journal:  Proc Biol Sci       Date:  1998-05-22       Impact factor: 5.349

Review 5.  Earth observation, geographic information systems and Plasmodium falciparum malaria in sub-Saharan Africa.

Authors:  S I Hay; J A Omumbo; M H Craig; R W Snow
Journal:  Adv Parasitol       Date:  2000       Impact factor: 3.870

6.  Utilization of combined remote sensing techniques to detect environmental variables influencing malaria vector densities in rural West Africa.

Authors:  Peter Dambach; Vanessa Machault; Jean-Pierre Lacaux; Cécile Vignolles; Ali Sié; Rainer Sauerborn
Journal:  Int J Health Geogr       Date:  2012-03-23       Impact factor: 3.918

7.  Pathogenic landscapes: interactions between land, people, disease vectors, and their animal hosts.

Authors:  Eric F Lambin; Annelise Tran; Sophie O Vanwambeke; Catherine Linard; Valérie Soti
Journal:  Int J Health Geogr       Date:  2010-10-27       Impact factor: 3.918

8.  Using high spatial resolution remote sensing for risk mapping of malaria occurrence in the Nouna district, Burkina Faso.

Authors:  Peter Dambach; Ali Sié; Jean-Pierre Lacaux; Cécile Vignolles; Vanessa Machault; Rainer Sauerborn
Journal:  Glob Health Action       Date:  2009-11-11       Impact factor: 2.640

Review 9.  Land cover, land use and malaria in the Amazon: a systematic literature review of studies using remotely sensed data.

Authors:  Aurélia Stefani; Isabelle Dusfour; Ana Paula S A Corrêa; Manoel C B Cruz; Nadine Dessay; Allan K R Galardo; Clícia D Galardo; Romain Girod; Margarete S M Gomes; Helen Gurgel; Ana Cristina F Lima; Eduardo S Moreno; Lise Musset; Mathieu Nacher; Alana C S Soares; Bernard Carme; Emmanuel Roux
Journal:  Malar J       Date:  2013-06-08       Impact factor: 2.979

10.  Using remote sensing to map larval and adult populations of Anopheles hyrcanus (Diptera: Culicidae) a potential malaria vector in Southern France.

Authors:  Annelise Tran; Nicolas Ponçon; Céline Toty; Catherine Linard; Hélène Guis; Jean-Baptiste Ferré; Danny Lo Seen; François Roger; Stéphane de la Rocque; Didier Fontenille; Thierry Baldet
Journal:  Int J Health Geogr       Date:  2008-02-26       Impact factor: 3.918

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