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Remote Sens. 2016, 8(4), 319; doi:10.3390/rs8040319

Mapping a Knowledge-Based Malaria Hazard Index Related to Landscape Using Remote Sensing: Application to the Cross-Border Area between French Guiana and Brazil

1
ESPACE-DEV, UMR 228 IRD/UM/UR/UG, Institut de Recherche pour le Développement, 500 rue Jean-François Breton, Montpellier 34000, France
2
Unité d’Entomologie Médicale, Institut Pasteur de la Guyane, 23 Avenue Pasteur BP 6010, Cayenne Cedex 97306, French Guiana
3
EPaT Team (EA 3593), UFR de Médecine—Université des Antilles et de la Guyane, Cayenne Cedex 97336, French Guiana
*
Authors to whom correspondence should be addressed.
Academic Editors: Zhong Lu and Prasad S. Thenkabail
Received: 8 February 2016 / Revised: 5 April 2016 / Accepted: 7 April 2016 / Published: 11 April 2016
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Abstract

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. View Full-Text
Keywords: remote sensing; land use and land cover; landscape metric; knowledge-based hazard modeling; malaria; cross-border area between French Guiana and Brazil remote sensing; land use and land cover; landscape metric; knowledge-based hazard modeling; malaria; cross-border area between French Guiana and Brazil
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Li, Z.; Roux, E.; Dessay, N.; Girod, R.; Stefani, A.; Nacher, M.; Moiret, A.; Seyler, F. Mapping a Knowledge-Based Malaria Hazard Index Related to Landscape Using Remote Sensing: Application to the Cross-Border Area between French Guiana and Brazil. Remote Sens. 2016, 8, 319.

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