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Predicting the distribution of canine leishmaniasis in western Europe based on environmental variables. | LitMetric

AI Article Synopsis

  • The study focuses on Leishmania infantum, which causes zoonotic visceral leishmaniasis and is commonly found within domestic dogs in southern Europe, particularly Mediterranean regions.
  • Researchers analyzed data from 2187 surveys on canine leishmaniasis (CanL) in countries like Portugal, Spain, France, and Italy, finding an overall seroprevalence of 23.2%.
  • Logistic regression models were used to identify environmental factors affecting CanL rates, with the France-only model performing better for predicting seroprevalence in France, while both models struggled with intermediate levels (5-20%) due to survey biases, suggesting a need for standardized survey methods for better risk mapping.

Article Abstract

The domestic dog is the reservoir host of Leishmania infantum, the causative agent of zoonotic visceral leishmaniasis endemic in Mediterranean Europe. Targeted control requires predictive risk maps of canine leishmaniasis (CanL), which are now explored. We databased 2187 published and unpublished surveys of CanL in southern Europe. A total of 947 western surveys met inclusion criteria for analysis, including serological identification of infection (504, 369 dogs tested 1971-2006). Seroprevalence was 23 2% overall (median 10%). Logistic regression models within a GIS framework identified the main environmental predictors of CanL seroprevalence in Portugal, Spain, France and Italy, or in France alone. A 10-fold cross-validation approach determined model capacity to predict point-values of seroprevalence and the correct seroprevalence class (<5%, 5-20%, >20%). Both the four-country and France-only models performed reasonably well for predicting correctly the <5% and >20% seroprevalence classes (AUC >0 70). However, the France-only model performed much better for France than the four-country model. The four-country model adequately predicted regions of CanL emergence in northern Italy (<5% seroprevalence). Both models poorly predicted intermediate point seroprevalences (5-20%) within regional foci, because surveys were biased towards known rural foci and Mediterranean bioclimates. Our recommendations for standardizing surveys would permit higher-resolution risk mapping.

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Source
http://dx.doi.org/10.1017/S003118201100148XDOI Listing

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