Cargando…

Risk Mapping of Anopheles gambiae s.l. Densities Using Remotely-Sensed Environmental and Meteorological Data in an Urban Area: Dakar, Senegal

INTRODUCTION: High malaria transmission heterogeneity in an urban environment is basically due to the complex distribution of Anopheles larval habitats, sources of vectors. Understanding 1) the meteorological and ecological factors associated with differential larvae spatio-temporal distribution and...

Descripción completa

Detalles Bibliográficos
Autores principales: Machault, Vanessa, Vignolles, Cécile, Pagès, Frédéric, Gadiaga, Libasse, Tourre, Yves M., Gaye, Abdoulaye, Sokhna, Cheikh, Trape, Jean-François, Lacaux, Jean-Pierre, Rogier, Christophe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3511318/
https://www.ncbi.nlm.nih.gov/pubmed/23226351
http://dx.doi.org/10.1371/journal.pone.0050674
_version_ 1782251581297655808
author Machault, Vanessa
Vignolles, Cécile
Pagès, Frédéric
Gadiaga, Libasse
Tourre, Yves M.
Gaye, Abdoulaye
Sokhna, Cheikh
Trape, Jean-François
Lacaux, Jean-Pierre
Rogier, Christophe
author_facet Machault, Vanessa
Vignolles, Cécile
Pagès, Frédéric
Gadiaga, Libasse
Tourre, Yves M.
Gaye, Abdoulaye
Sokhna, Cheikh
Trape, Jean-François
Lacaux, Jean-Pierre
Rogier, Christophe
author_sort Machault, Vanessa
collection PubMed
description INTRODUCTION: High malaria transmission heterogeneity in an urban environment is basically due to the complex distribution of Anopheles larval habitats, sources of vectors. Understanding 1) the meteorological and ecological factors associated with differential larvae spatio-temporal distribution and 2) the vectors dynamic, both may lead to improving malaria control measures with remote sensing and high resolution data as key components. In this study a robust operational methodology for entomological malaria predictive risk maps in urban settings is developed. METHODS: The Tele-epidemiology approach, i.e., 1) intensive ground measurements (Anopheles larval habitats and Human Biting Rate, or HBR), 2) selection of the most appropriate satellite data (for mapping and extracting environmental and meteorological information), and 3) use of statistical models taking into account the spatio-temporal data variability has been applied in Dakar, Senegal. RESULTS: First step was to detect all water bodies in Dakar. Secondly, environmental and meteorological conditions in the vicinity of water bodies favoring the presence of Anopheles gambiae s.l. larvae were added. Then relationship between the predicted larval production and the field measured HBR was identified, in order to generate An. gambiae s.l. HBR high resolution maps (daily, 10-m pixel in space). DISCUSSION AND CONCLUSION: A robust operational methodology for dynamic entomological malaria predictive risk maps in an urban setting includes spatio-temporal variability of An. gambiae s.l. larval habitats and An. gambiae s.l. HBR. The resulting risk maps are first examples of high resolution products which can be included in an operational warning and targeting system for the implementation of vector control measures.
format Online
Article
Text
id pubmed-3511318
institution National Center for Biotechnology Information
language English
publishDate 2012
publisher Public Library of Science
record_format MEDLINE/PubMed
spelling pubmed-35113182012-12-05 Risk Mapping of Anopheles gambiae s.l. Densities Using Remotely-Sensed Environmental and Meteorological Data in an Urban Area: Dakar, Senegal Machault, Vanessa Vignolles, Cécile Pagès, Frédéric Gadiaga, Libasse Tourre, Yves M. Gaye, Abdoulaye Sokhna, Cheikh Trape, Jean-François Lacaux, Jean-Pierre Rogier, Christophe PLoS One Research Article INTRODUCTION: High malaria transmission heterogeneity in an urban environment is basically due to the complex distribution of Anopheles larval habitats, sources of vectors. Understanding 1) the meteorological and ecological factors associated with differential larvae spatio-temporal distribution and 2) the vectors dynamic, both may lead to improving malaria control measures with remote sensing and high resolution data as key components. In this study a robust operational methodology for entomological malaria predictive risk maps in urban settings is developed. METHODS: The Tele-epidemiology approach, i.e., 1) intensive ground measurements (Anopheles larval habitats and Human Biting Rate, or HBR), 2) selection of the most appropriate satellite data (for mapping and extracting environmental and meteorological information), and 3) use of statistical models taking into account the spatio-temporal data variability has been applied in Dakar, Senegal. RESULTS: First step was to detect all water bodies in Dakar. Secondly, environmental and meteorological conditions in the vicinity of water bodies favoring the presence of Anopheles gambiae s.l. larvae were added. Then relationship between the predicted larval production and the field measured HBR was identified, in order to generate An. gambiae s.l. HBR high resolution maps (daily, 10-m pixel in space). DISCUSSION AND CONCLUSION: A robust operational methodology for dynamic entomological malaria predictive risk maps in an urban setting includes spatio-temporal variability of An. gambiae s.l. larval habitats and An. gambiae s.l. HBR. The resulting risk maps are first examples of high resolution products which can be included in an operational warning and targeting system for the implementation of vector control measures. Public Library of Science 2012-11-30 /pmc/articles/PMC3511318/ /pubmed/23226351 http://dx.doi.org/10.1371/journal.pone.0050674 Text en © 2012 Machault et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Machault, Vanessa
Vignolles, Cécile
Pagès, Frédéric
Gadiaga, Libasse
Tourre, Yves M.
Gaye, Abdoulaye
Sokhna, Cheikh
Trape, Jean-François
Lacaux, Jean-Pierre
Rogier, Christophe
Risk Mapping of Anopheles gambiae s.l. Densities Using Remotely-Sensed Environmental and Meteorological Data in an Urban Area: Dakar, Senegal
title Risk Mapping of Anopheles gambiae s.l. Densities Using Remotely-Sensed Environmental and Meteorological Data in an Urban Area: Dakar, Senegal
title_full Risk Mapping of Anopheles gambiae s.l. Densities Using Remotely-Sensed Environmental and Meteorological Data in an Urban Area: Dakar, Senegal
title_fullStr Risk Mapping of Anopheles gambiae s.l. Densities Using Remotely-Sensed Environmental and Meteorological Data in an Urban Area: Dakar, Senegal
title_full_unstemmed Risk Mapping of Anopheles gambiae s.l. Densities Using Remotely-Sensed Environmental and Meteorological Data in an Urban Area: Dakar, Senegal
title_short Risk Mapping of Anopheles gambiae s.l. Densities Using Remotely-Sensed Environmental and Meteorological Data in an Urban Area: Dakar, Senegal
title_sort risk mapping of anopheles gambiae s.l. densities using remotely-sensed environmental and meteorological data in an urban area: dakar, senegal
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3511318/
https://www.ncbi.nlm.nih.gov/pubmed/23226351
http://dx.doi.org/10.1371/journal.pone.0050674
work_keys_str_mv AT machaultvanessa riskmappingofanophelesgambiaesldensitiesusingremotelysensedenvironmentalandmeteorologicaldatainanurbanareadakarsenegal
AT vignollescecile riskmappingofanophelesgambiaesldensitiesusingremotelysensedenvironmentalandmeteorologicaldatainanurbanareadakarsenegal
AT pagesfrederic riskmappingofanophelesgambiaesldensitiesusingremotelysensedenvironmentalandmeteorologicaldatainanurbanareadakarsenegal
AT gadiagalibasse riskmappingofanophelesgambiaesldensitiesusingremotelysensedenvironmentalandmeteorologicaldatainanurbanareadakarsenegal
AT tourreyvesm riskmappingofanophelesgambiaesldensitiesusingremotelysensedenvironmentalandmeteorologicaldatainanurbanareadakarsenegal
AT gayeabdoulaye riskmappingofanophelesgambiaesldensitiesusingremotelysensedenvironmentalandmeteorologicaldatainanurbanareadakarsenegal
AT sokhnacheikh riskmappingofanophelesgambiaesldensitiesusingremotelysensedenvironmentalandmeteorologicaldatainanurbanareadakarsenegal
AT trapejeanfrancois riskmappingofanophelesgambiaesldensitiesusingremotelysensedenvironmentalandmeteorologicaldatainanurbanareadakarsenegal
AT lacauxjeanpierre riskmappingofanophelesgambiaesldensitiesusingremotelysensedenvironmentalandmeteorologicaldatainanurbanareadakarsenegal
AT rogierchristophe riskmappingofanophelesgambiaesldensitiesusingremotelysensedenvironmentalandmeteorologicaldatainanurbanareadakarsenegal