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Home / Publicações / Predicting the distribution of canine leishmaniasis in western Europe based on environmental variables.

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

  • Autores: Afonso MO, Ballart C, Barón-López S, Cox J, Davies CR, Dedet JP, Franco AO, Gállego M, Gálvez R, Gradoni L, Gramiccia M, Molina R, Morillas-Márquez F, Mylne A, Pires CA, Ready PD
  • Ano de Publicação: 2011
  • Journal: Parasitology
  • Link: http://www.ncbi.nlm.nih.gov/pubmed/?term=Predicting+the+distribution+of+canine+leishmaniasis+in+Western+Europe+based+on+environmental+variables

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 We databased 2187 published and unpublished surveys of CanL in southern Europe.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.

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

  • Autores: Afonso MO, Ballart C, Barón-López S, Cox J, Davies CR, Dedet JP, Franco AO, Gállego M, Gálvez R, Gradoni L, Gramiccia M, Molina R, Morillas-Márquez F, Mylne A, Pires CA, Ready PD
  • Ano de Publicação: 2011
  • Journal: Parasitology
  • Link: http://www.ncbi.nlm.nih.gov/pubmed/?term=Predicting+the+distribution+of+canine+leishmaniasis+in+Western+Europe+based+on+environmental+variables

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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