Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
Background: Heterogeneous patterns of malaria transmission are thought to be driven by factors including host genetics, distance to mosquito breeding sites, housing construction, and socio-behavioural characteristics. Evaluation of local transmission epidemiology to characterize malaria risk is essential for planning malaria control and elimination programmes. The use of geographical information systems (GIS) techniques has been a major asset to this approach. To assess time and space distribution of malaria disease in Bandiagara, Mali, within a transmission season, data were used from an ongoing malaria incidence study that enrolled 300 participants aged under six years old ". Methods: Children's households were georeferenced using a handheld global position system. Clinical malaria was defined as a positive blood slide for Plasmodium falciparum asexual stages associated with at least one of the following signs: headache, body aches, fever, chills and weakness. Daily rainfall was measured at the local weather station. Landscape features of Bandiagara were obtained from satellite images and field survey. QGIS™ software was used to map malaria cases, affected and non-affected children, and the number of malaria episodes per child in each block of Bandiagara. Clusters of high or low risk were identified under SaTScan W software according to a Bernoulli model. Results: From June 2009 to May 2010, 296 clinical malaria cases were recorded. Though clearly temporally related to the rains, Plasmodium falciparum occurrence persisted late in the dry season. Two " hot spots " of malaria transmission also found, notably along the Yamé River, characterized by higher than expected numbers of malaria cases, and high numbers of clinical episodes per child. Conversely, the northeastern sector of the town had fewer cases despite its proximity to a large body of standing water which was mosquito habitat. Conclusion: These results confirm the existence of a marked spatial heterogeneity of malaria transmission in Bandiagara, providing support for implementation of targeted interventions. Background Malaria is one of the leading causes of morbidity and mortality in the world, with an estimated 3.3 billion people at risk of malaria [1]. The incidence of malaria worldwide is estimated to be 216 million cases per year, with 81% of these cases occurring in sub-Saharan Africa. Malaria kills approximately 655,000 people per year; 91% of deaths occur in sub-Saharan Africa [1], mostly in children under five years of age. In Mali, West Africa, malaria represents 36.5% of consultation motives in health center, it is a leading cause of morbidity and mortality children of less than five years of age and the first reason of anaemia in pregnant women [2]. Malaria transmission is seasonal. Malaria parasite transmission and clinical disease are characterized by important microgeographic variation, often between adjacent villages, households or families [3-8]. This local heterogeneity is driven by a variety of factors including human genetics [9,10], distance to potential breeding sites [11,12], housing construction
ISSN: 1475-2875

hal-01307672
https://hal-amu.archives-ouvertes.fr/hal-01307672
https://hal-amu.archives-ouvertes.fr/hal-01307672/document
https://hal-amu.archives-ouvertes.fr/hal-01307672/file/1475-2875-12-82.pdf
DOI : 10.1186/1476-072X-2-5

Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
BACKGROUND: Sampling design is a key issue when establishing species inventories and characterizing habitats within highly heterogeneous landscapes. Sampling efforts in such environments may be constrained and many field studies only rely on subjective and/or qualitative approaches to design collection strategy. The region of Cacao, in French Guiana, provides an excellent study site to understand the presence and abundance of Anopheles mosquitoes, their species dynamics and the transmission risk of malaria across various environments. We propose an objective methodology to define a stratified sampling design. Following thorough environmental characterization, a factorial analysis of mixed groups allows the data to be reduced and non-collinear principal components to be identified while balancing the influences of the different environmental factors. Such components defined new variables which could then be used in a robust k-means clustering procedure. Then, we identified five clusters that corresponded to our sampling strata and selected sampling sites in each stratum. RESULTS: We validated our method by comparing the species overlap of entomological collections from selected sites and the environmental similarities of the same sites. The Morisita index was significantly correlated (Pearson linear correlation) with environmental similarity based on i) the balanced environmental variable groups considered jointly (p = 0.001) and ii) land cover/use (p-value<0.001). The Jaccard index was significantly correlated with land cover/use-based environmental similarity (p-value = 0.001). CONCLUSIONS: The results validate our sampling approach. Land cover/use maps (based on high spatial resolution satellite images) were shown to be particularly useful when studying the presence, density and diversity of Anopheles mosquitoes at local scales and in very heterogeneous landscapes.
BMC Ecology

pasteur-00914104
https://hal-riip.archives-ouvertes.fr/pasteur-00914104
https://hal-riip.archives-ouvertes.fr/pasteur-00914104/document
DOI : 10.1186/1472-6785-13-45
PUBMED : 24289184

Éditeur(s) : HAL CCSD BioMed Central
Résumé : International audience
The nine countries sharing the Amazon forest accounted for 89% of all malaria cases reported in the Americas in 2008. Remote sensing can help identify the environmental determinants of malaria transmission and their temporo-spatial evolution. Seventeen studies characterizing land cover or land use features, and relating them to malaria in the Amazon subregion, were identified. These were reviewed in order to improve the understanding of the land cover/use class roles in malaria transmission. The indicators affecting the transmission risk were summarized in terms of temporal components, landscape fragmentation and anthropic pressure. This review helps to define a framework for future studies aiming to characterize and monitor malaria.
ISSN: 1475-2875

inserm-00834790
http://www.hal.inserm.fr/inserm-00834790
http://www.hal.inserm.fr/inserm-00834790/document
http://www.hal.inserm.fr/inserm-00834790/file/1475-2875-12-192.pdf
DOI : 10.1186/1475-2875-12-192

Éditeur(s) : HAL CCSD MDPI
Résumé : International audience
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
ISSN: 2072-4292

hal-01355255
https://hal.archives-ouvertes.fr/hal-01355255
https://hal.archives-ouvertes.fr/hal-01355255/document
https://hal.archives-ouvertes.fr/hal-01355255/file/010066865.pdf
IRD : fdi:010066865

Éditeur(s) : HAL CCSD Public Library of Science
Résumé : International audience
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.
ISSN: 1932-6203

hal-01383486
https://hal.archives-ouvertes.fr/hal-01383486
https://hal.archives-ouvertes.fr/hal-01383486/document
https://hal.archives-ouvertes.fr/hal-01383486/file/Adde_et_al_PlosONE_2016.pdf
DOI : 10.1371/journal.pone.0164685