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PLoS Negl Trop Dis. 2019 Aug 14;13(8):e0007629. doi: 10.1371/journal.pntd.0007629. eCollection 2019 Aug.

Ecological niche modelling for predicting the risk of cutaneous leishmaniasis in the Neotropical moist forest biome.

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Laboratoire des Interactions Virus-Hôtes, Institut Pasteur de la Guyane, Cayenne, French Guiana.
Laboratoire des Ecosystèmes Amazoniens et Pathologie Tropicale, EA3593, Medicine Department, Université de Guyane, Cayenne, French Guiana.
Laboratório de Ecologia de Doenças Transmissíveis na Amazônia, EDTA Instituto Lêonidas e Maria Deane, FIOCRUZ, Amazonas, Brazil.
Grupo de Investigaciones Microbiológicas-UR (GIMUR), Programa de Biología, Facultad de Ciencias Naturales y Matemáticas, Universidad del Rosario, Bogotá, Colombia.
Parasitology Unit, Instituto Evandro Chagas (Secretaria de Vigilância em Saúde, Ministério da Saúde), Ananindeua, Brazil.
Laboratoire Associé du CNR Leishmaniose, Laboratoire Hospitalo-Universitaire de Parasitologie-Mycologie, Centre Hospitalier Andrée Rosemon, Cayenne, French Guiana.
Unité Mixte de Recherche MIVEGEC, Université de Montpellier, IRD, CNRS, Montpellier, France.
Unité Mixte de Recherche ASTRE Cirad-INRA, Université de Montpellier, Montpellier, France.


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.

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