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Nous vous invitons à venir nombreux au prochain séminaire du MIVEGEC ce jeudi 23 de mars à 11h00 dans l'amphithéâtre des plantes du centre IRD de Montpellier,

Zhichao Li, Doctorant dirigé par Emmnuel Roux et Nadine Dessay de la Maison de teledetection/IRD, nous fera une présentation intitulée :

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

 

 

Résumé de la présentation :

Malaria remains one of the most common vector-borne diseases in the world. Identifying malaria risk factors and modeling malaria transmission processes can be conducive to the definition of novel control strategies of malaria. However, data-driven models are often difficult to build, as data are very often incomplete, heterogeneous in nature and in quality, and/or biased, especially in these remote areas where epidemiological, entomological, and environmental monitoring are inefficient and/or irregular. 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 (percent of forest and edge density between forest and non-forest patches) to build a normalized landscape-based hazard index (NLHI). Such 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. More generally, for each location in the forest vs. non-forest landscape, the percentage of forest is higher and the border of forest vs. non-forest areas is more complex, the chance of human-vector encounters is higher. 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.

 

 


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