Abstract
A major challenge of eco-epidemiology is to determine which factors promote the transmission of infectious diseases and to establish risk maps that can be used by public health authorities. The geographic predictions resulting from ecological niche modelling have been widely used for modelling the future dispersion of vectors based on the occurrence records and the potential prevalence of the disease. The establishment of risk maps for disease systems with complex cycles such as cutaneous leishmaniasis (CL) can be very challenging due to the many inference networks between large sets of host and vector species, with considerable heterogeneity in disease patterns in space and time. One novelty in the present study is the use of human CL cases to predict the risk of leishmaniasis occurrence in response to anthropogenic, climatic and environmental factors at two different scales, in the Neotropical moist forest biome (Amazonian basin and surrounding forest ecosystems) and in the surrounding region of French Guiana. With a consistent data set never used before and a conceptual and methodological framework for interpreting data cases, we obtained risk maps with high statistical support. The predominantly identified human CL risk areas are those where the human impact on the environment is significant, associated with less contributory climatic and ecological factors. For both models this study highlights the importance of considering the anthropogenic drivers for disease risk assessment in human, although CL is mainly linked to the sylvatic and peri-urban cycle in Meso and South America.
Highlights
Vector-borne diseases that threaten one-third of the world’s population are driven by intertwined socio-economic and environmental factors, such as climate change and modifications of ecosystems through deforestation, conversion of natural habitats to man-made ecosystems and extended urbanisation [1]
The three different methods for Amazonian and French Guiana models show only very few differences in their respective area under the curve (AUC), and the occurrence of cutaneous leishmaniasis (CL) cases is explained by the same set of environmental and anthropogenic variables (S1 Table)
The potential input value of ecological niche modelling (ENM) for spatial epidemiology is widely accepted for generating risk maps and answering ecological and distributional questions related to the disease system, its persistence and spread [50]
Summary
Vector-borne diseases that threaten one-third of the world’s population are driven by intertwined socio-economic and environmental factors, such as climate change and modifications of ecosystems through deforestation, conversion of natural habitats to man-made ecosystems and extended urbanisation [1] To understand these disease agent dynamics, it is necessary to determine (1) the geographic area and associated ecological conditions where the transmission cycle could likely occur, with the infected vectors and host reservoirs, (2) the risk factors that promote transmission to humans and (3) the human communities that are the most exposed to infection hazards on a local scale [1,2,3]. Landscape ecology may contribute to the knowledge of the influence of biotic and abiotic factors on the presence and dynamics of the vectors and host reservoirs [4] It favours the development of spatial models of risk prediction at a relevant geographic scale [5], which finds its theoretical and more practical extensions within the new pathogeography paradigm [6]. Vulnerability represents the individual and group conditions that make humans more sensitive to infection, e.g., genetic susceptibility or malnourished people [9]
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