untitled
<OAI-PMH schemaLocation=http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd>
<responseDate>2018-01-15T18:22:26Z</responseDate>
<request identifier=oai:HAL:hal-01355255v1 verb=GetRecord metadataPrefix=oai_dc>http://api.archives-ouvertes.fr/oai/hal/</request>
<GetRecord>
<record>
<header>
<identifier>oai:HAL:hal-01355255v1</identifier>
<datestamp>2018-01-12</datestamp>
<setSpec>type:ART</setSpec>
<setSpec>subject:stat</setSpec>
<setSpec>subject:sdv</setSpec>
<setSpec>subject:shs</setSpec>
<setSpec>collection:UNIV-AG</setSpec>
<setSpec>collection:SANTE_PUB_INSERM</setSpec>
<setSpec>collection:AO-GEOGRAPHIE</setSpec>
<setSpec>collection:AGROPOLIS</setSpec>
<setSpec>collection:SHS</setSpec>
<setSpec>collection:GIP-BE</setSpec>
<setSpec>collection:GUYANE</setSpec>
<setSpec>collection:IRD</setSpec>
<setSpec>collection:REDIAL</setSpec>
<setSpec>collection:UNICE</setSpec>
<setSpec>collection:AAE-REVISTA</setSpec>
<setSpec>collection:UNIV-PERP</setSpec>
<setSpec>collection:UNIV-AVIGNON</setSpec>
<setSpec>collection:MIPS</setSpec>
<setSpec>collection:UNIV-MONTPELLIER</setSpec>
<setSpec>collection:ESPACE-DEV</setSpec>
<setSpec>collection:UCA-TEST</setSpec>
<setSpec>collection:UNIV-COTEDAZUR</setSpec>
</header>
<metadata><dc>
<publisher>HAL CCSD</publisher>
<title lang=en>Mapping a Knowledge-Based Malaria Hazard Index Related to Landscape Using Remote Sensing: Application to the Cross-Border Area between French Guiana and Brazil</title>
<creator>Li, Zhichao</creator>
<creator>Roux, Emmanuel</creator>
<creator>Dessay, Nadine</creator>
<creator>Girod, Romain</creator>
<creator>Stefani, Aurélia</creator>
<creator>Nacher, Mathieu</creator>
<creator>Moiret, Adrien</creator>
<creator>Seyler, Frédérique</creator>
<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>Expertise et spatialisation des connaissances en environnement (ESPACE)</contributor>
<contributor>Unité d'Entomologie Médicale ; Institut Pasteur de la Guyane</contributor>
<contributor>Medicine Department ; Ecosystemes Amazoniens et Pathologie Tropicale (EPat) ; Institut Pasteur de la Guyane - Institut National de la Santé et de la Recherche Médicale (INSERM) - Université de Guyane (UG) - Institut Pasteur de la Guyane - Institut National de la Santé et de la Recherche Médicale (INSERM) - Université de Guyane (UG)</contributor>
<contributor>Centre d'Investigation Clinique Antilles-Guyane (CIC - Antilles Guyane) ; Université des Antilles et de la Guyane (UAG) - Institut National de la Santé et de la Recherche Médicale (INSERM) - CHU de Pointe-à-Pitre - Centre Hospitalier de Cayenne Andrée Rosemon - CHU de Fort de France</contributor>
<description>International audience</description>
<source>ISSN: 2072-4292</source>
<source>Remote Sensing</source>
<publisher>MDPI</publisher>
<identifier>hal-01355255</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01355255</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01355255/document</identifier>
<identifier>https://hal.archives-ouvertes.fr/hal-01355255/file/010066865.pdf</identifier>
<source>https://hal.archives-ouvertes.fr/hal-01355255</source>
<source>Remote Sensing, MDPI, 2016, 8 (4), 〈10.3390/rs8040319〉</source>
<identifier>IRD : fdi:010066865</identifier>
<identifier>DOI : 10.3390/rs8040319</identifier>
<relation>info:eu-repo/semantics/altIdentifier/doi/10.3390/rs8040319</relation>
<language>en</language>
<subject lang=en>malaria</subject>
<subject lang=en>cross-border area between French Guiana and Brazil</subject>
<subject lang=en>remote sensing</subject>
<subject lang=en>land use and land cover</subject>
<subject lang=en>landscape metric</subject>
<subject lang=en>knowledge-based hazard modeling</subject>
<subject>[STAT.AP] Statistics [stat]/Applications [stat.AP]</subject>
<subject>[SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie</subject>
<subject>[SHS.GEO] Humanities and Social Sciences/Geography</subject>
<type>info:eu-repo/semantics/article</type>
<type>Journal articles</type>
<description lang=en>Malaria remains one of the most common vector-borne diseases in the world and the definition of novel control strategies can benefit from the modeling of transmission processes. However, data-driven models are often difficult to build, as data are very often incomplete, heterogeneous in nature and in quality, and/or biased. In this context, a knowledge-based approach is proposed to build a robust and general landscape-based hazard index for malaria transmission that is tailored to the Amazonian region. A partial knowledge-based model of the risk of malaria transmission in the Amazonian region, based on landscape features and extracted from a systematic literature review, was used. Spatialization of the model was obtained by generating land use and land cover maps of the cross-border area between French Guiana and Brazil, followed by computing and combining landscape metrics to build a set of normalized landscape-based hazard indices. An empirical selection of the best index was performed by comparing the indices in terms of adequacy with the knowledge-based model, intelligibility and correlation with P. falciparum incidence rates. The selected index is easy to interpret and successfully represents the current knowledge about the role played by landscape patterns in malaria transmission within the study area. It was significantly associated with P. falciparum incidence rates, using the Pearson and Spearman correlation coefficients (up to 0.79 and 0.75, respectively; p-value < 0.001), and the linear regression coefficient of determination (reaching 0.63; p-values < 0.001). This study establishes a spatial knowledge-driven, landscape-based hazard malaria index using remote sensing that can be easily produced on a regular basis and might be useful for malaria prediction, surveillance, and control. Keywords: remote sensing; land use and land cover; landscape metric; knowledge-based hazard modeling; malaria; cross-border area between French Guiana and Brazil</description>
<date>2016-04-11</date>
</dc>
</metadata>
</record>
</GetRecord>
</OAI-PMH>
