Land cover, land use and malaria in the Amazon: a systematic literature review of studies using remotely sensed data

Abstract

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.

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

“…The fact that some other indices computed with SPLIT and DIVISION metrics give higher coefficients does not invalidate the proposed one, because the malaria incidence rates (resulting from environmental and human factors that are not taken into account by the NLHI) cannot, by themselves, definitely and objectively determine which one is the best. The excellent correlations found between the possible indices and P. falciparum incidence rates can be explained by the fact that: (i) the NLHI implements a knowledge-based model that realizes a consensual synthesis of the conclusions of all the studies reviewed by Stefani et al [14] and related to the impacts of land cover, land use, and landscape structure to malaria transmission; and (ii) the village of Camopi has quite homogeneous environmental and societal contexts where the landscape features play a key role in vector-human encounters and, thus, in malaria transmission. As previously discussed in the Quantitative Evaluation section, the hypothesis that the P. falciparum incidence rates in Camopi are largely explained by the landscape features, in this particular context, justifies the use of incidence rates as evaluation data.…”

“…From an ecological standpoint, the knowledge-based model (Figure 2) is likely to represent the following process [14,44] where: (i) deforested areas provide favorable conditions for malaria vector breeding and feeding; and (ii) forest and secondary forest can define resting sites for adult mosquitoes that return to the forest and secondary forest after feeding. Consequently, the more the forest and secondary forest patches interact with deforested patches (situation described in the bottom-right box of Figure 2), the more the landscape is favorable to vectors and vector-human being encounters.…”

“…De Glanville et al [28] predicted the suitability for African swine fever (ASF) endemicity in Africa using multi-criteria decision analysis, and validated its performance via statistical methods using available ASF outbreak data. Stefani et al did a systematic review of the literature [14], and proposed a consensual knowledge-based model that qualitatively estimates the level of malaria transmission risk in the Amazon in relation to deforestation patterns. Knowledge-based risk modeling can elaborate the relationship between malaria vectors and environmental variables, explain interactions between environmental changes and vector dynamics, and provide a knowledge base to demonstrate how the environment affects malaria transmission [31].…”

“…darlingi) are involved, since the presence and the vector density strongly depend on the presence and the density of human beings. Stefani et al did a systematic review of the literature [14], and proposed a consensual knowledge-based model that qualitatively estimates the level of malaria transmission risk in the Amazon in relation to deforestation patterns. More precisely, this model demonstrated that landscape features play a key role in the malaria risk construction, by generating more or less favorable conditions for the encounter between mosquitoes and human beings.…”

“…Moreover, the species shows a high efficiency in malaria transmission through high biting rates, susceptibility to Plasmodium infection and capacity to easily adapt to environmental changes due to human activities [5,9]. Natural and human-driven environmental changes, such as climate variability [10][11][12][13] and land use and land cover (LULC) changes [13][14][15][16][17][18][19], can determine the malaria distribution by influencing the habitats of adults and larvae. Prevention and treatment measures [20] and mobility of humans infected by Plasmodium [4,15] also influence such distribution.…”