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<publisher>HAL CCSD</publisher>
<title lang=en>Dynamical Mapping of Anopheles darlingi Densities in a Residual Malaria Transmission Area of French Guiana by Using Remote Sensing and Meteorological Data</title>
<creator>Adde, Antoine</creator>
<creator>Roux, Emmanuel</creator>
<creator>Mangeas, Morgan</creator>
<creator>Dessay, Nadine</creator>
<creator>Nacher, Mathieu</creator>
<creator>Dusfour, Isabelle</creator>
<creator>Girod, Romain</creator>
<creator>Briolant, Sébastien</creator>
<contributor>Unité d'Epidémiologie ; Institut Pasteur de la Guyane - Réseau International des Instituts Pasteur (RIIP)</contributor>
<contributor>Unité d'Entomologie Médicale ; Institut Pasteur de la Guyane</contributor>
<contributor>UMR 228 Espace-Dev, Espace pour le développement ; Institut de Recherche pour le Développement (IRD) - Université de Perpignan Via Domitia (UPVD) - Université Nice Sophia Antipolis (UNS) ; Université Côte d'Azur (UCA) - Université Côte d'Azur (UCA) - Université d'Avignon et des Pays de Vaucluse (UAPV) - Université de la Réunion (UR) - Université de Montpellier (UM) - Université de Guyane (UG) - Université des Antilles (Pôle Martinique) ; Université des Antilles (UA) - Université des Antilles (UA) - Université des Antilles (Pôle Guadeloupe) ; Université des Antilles (UA)</contributor>
<contributor>Université des Antilles et de la Guyane (UAG)</contributor>
<contributor>Institut Pasteur de la Guyane</contributor>
<contributor>Unité de Recherche sur les Maladies Infectieuses et Tropicales Emergentes (URMITE) ; Centre National de la Recherche Scientifique (CNRS) - IFR48 - Institut National de la Santé et de la Recherche Médicale (INSERM) - Aix Marseille Université (AMU) - Institut de Recherche pour le Développement (IRD)</contributor>
<contributor>Direction Interarmées du Service de Santé en Guyane</contributor>
<contributor>Institut de Recherches Biomédicales des Armées</contributor>
<description>International audience</description>
<source>ISSN: 1932-6203</source>
<source>PLoS ONE</source>
<publisher>Public Library of Science</publisher>
<identifier>hal-01383486</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01383486</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01383486/document</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01383486/file/Adde_et_al_PlosONE_2016.pdf</identifier>
<source>https://hal.archives-ouvertes.fr/hal-01383486</source>
<source>PLoS ONE, Public Library of Science, 2016, 11 (10), pp.e0164685. 〈10.1371/journal.pone.0164685〉</source>
<identifier>DOI : 10.1371/journal.pone.0164685</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pone.0164685</relation>
<language>en</language>
<subject lang=en>Vector-borne disease</subject>
<subject lang=en>Malaria</subject>
<subject lang=en>Climate</subject>
<subject lang=en>Meteorologic factors</subject>
<subject lang=en>Enviromental factors</subject>
<subject lang=en>French Guiana</subject>
<subject lang=en>Amazonian</subject>
<subject lang=en>Vector ecology</subject>
<subject lang=en>Modeling</subject>
<subject lang=en>Remote sensing</subject>
<subject lang=en>Dynamic model</subject>
<subject>[SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie</subject>
<subject>[SDV.MHEP.MI] Life Sciences [q-bio]/Human health and pathology/Infectious diseases</subject>
<subject>[SDV.EE] Life Sciences [q-bio]/Ecology, environment</subject>
<subject>[SDE.IE] Environmental Sciences/Environmental Engineering</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en>Local variation in the density of Anopheles mosquitoes and the risk of exposure to bites are essential to explain the spatial and temporal heterogeneities in the transmission of malaria. Vector distribution is driven by environmental factors. Based on variables derived from satellite imagery and meteorological observations, this study aimed to dynamically model and map the densities of Anopheles darlingi in the municipality of Saint-Georges de l’Oyapock (French Guiana). Longitudinal sampling sessions of An. darlingi densities were conducted between September 2012 and October 2014. Landscape and meteorological data were collected and processed to extract a panel of variables that were potentially related to An. darlingi ecology. Based on these data, a robust methodology was formed to estimate a statistical predictive model of the spatial-temporal variations in the densities of An. darlingi in Saint-Georges de l’Oyapock. The final cross-validated model integrated two landscape variables—dense forest surface and built surface—together with four meteorological variables related to rainfall, evapotranspiration, and the minimal and maximal temperatures. Extrapolation of the model allowed the generation of predictive weekly maps of An. darlingi densities at a resolution of 10-m. Our results supported the use of satellite imagery and meteorological data to predict malaria vector densities. Such fine-scale modeling approach might be a useful tool for health authorities to plan control strategies and social communication in a cost-effective, targeted, and timely manner.</description>
<date>2016-10-17</date>
</dc>
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