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Published on: February 20, 2020
Environmental risk mapping of canine leishmaniasis in France
Lise Chamaillé1, Annelise Tran, Anne Meunier
1Université Montpellier 1, Laboratoire de Parasitologie, Centre National de Référence des Leishmania, CHU de Montpellier and UMR 2724 GEMI (IRD-CNRS-UM1), Montpellier, France. parasito@univ-montp1.fr.
Background:
Canine leishmaniasis (CanL) is a zoonotic disease caused by Leishmania infantum, a Trypanosomatid protozoan transmitted by phlebotomine sandflies. Leishmaniasis is endemic in southern France, but the influences of environmental and climatic factors on its maintenance and emergence remain poorly understood. From a retrospective database, including all the studies reporting prevalence or incidence of CanL in France between 1965 and 2007, we performed a spatial analysis in order to i) map the reported cases in France, and ii) produce an environment-based map of the areas at risk for CanL. We performed a Principal Component Analysis (PCA) followed by a Hierarchical Ascendant Classification (HAC) to assess if the locations of CanL could be grouped according to environmental variables related to climate, forest cover, and human and dog densities. For each group, the potential distribution of CanL in France was mapped using a species niche modelling approach (Maxent model).
Results:
Results revealed the existence of two spatial groups of CanL cases. The first group is located in the Cévennes region (southern Massif Central), at altitudes of 200-1000 m above sea level, characterized by relatively low winter temperatures (1.9 degrees C average), 1042 mm average annual rainfall and much forest cover. The second group is located on the Mediterranean coastal plain, characterized by higher temperatures, lower rainfall and less forest cover. These two groups may correspond to the environments favoured by the two sandfly vectors in France, Phlebotomus ariasi and Phlebotomus perniciosus respectively. Our niche modelling of these two eco-epidemiological patterns was based on environmental variables and led to the first risk map for CanL in France.
Conclusion:
Results show how an ecological approach can help to improve our understanding of the spatial distribution of CanL in France.
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