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

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 We databased 2187 published and unpublished surveys of CanL in southern Europe.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

[1]  S. Mahamdallie,et al.  Integrated Mapping of Establishment Risk for Emerging Vector-Borne Infections: A Case Study of Canine Leishmaniasis in Southwest France , 2011, PloS one.

[2]  S. Mahamdallie,et al.  Multiple genetic divergences and population expansions of a Mediterranean sandfly, Phlebotomus ariasi, in Europe during the Pleistocene glacial cycles , 2011, Heredity.

[3]  R. Molina,et al.  Seasonal trends and spatial relations between environmental/meteorological factors and leishmaniosis sand fly vector abundances in Central Spain. , 2010, Acta tropica.

[4]  R. Molina,et al.  Emerging trends in the seroprevalence of canine leishmaniasis in the Madrid region (central Spain). , 2010, Veterinary parasitology.

[5]  A. Tran,et al.  Environmental risk mapping of canine leishmaniasis in France , 2010, Parasites & Vectors.

[6]  P. Ready Leishmaniasis emergence in Europe. , 2010, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[7]  M. Boelaert,et al.  Control of Visceral Leishmaniasis in Latin America—A Systematic Review , 2010, PLoS neglected tropical diseases.

[8]  O. Courtenay,et al.  Transmission, reservoir hosts and control of zoonotic visceral leishmaniasis , 2009, Parasitology.

[9]  Andrew J Tatem,et al.  Correction: A World Malaria Map: Plasmodium falciparum Endemicity in 2007 , 2009, PLoS Medicine.

[10]  J. Martín‐Sánchez,et al.  Canine Leishmaniasis in Southeastern Spain , 2009, Emerging infectious diseases.

[11]  David L. Smith,et al.  A World Malaria Map: Plasmodium falciparum Endemicity in 2007 , 2009, PLoS medicine.

[12]  L. Campino,et al.  Methods for diagnosis of canine leishmaniasis and immune response to infection. , 2008, Veterinary parasitology.

[13]  Simon Brooker,et al.  Human Helminth Co-Infection: Analysis of Spatial Patterns and Risk Factors in a Brazilian Community , 2008, PLoS neglected tropical diseases.

[14]  Ready Pd,et al.  Leishmaniasis emergence and climate change. , 2008 .

[15]  L. Gradoni,et al.  Spread of Vector-borne Diseases and Neglect of Leishmaniasis, Europe , 2008, Emerging infectious diseases.

[16]  Peter M Atkinson,et al.  Developing geostatistical space-time models to predict outpatient treatment burdens from incomplete national data. , 2008, Geographical analysis.

[17]  M. Gramiccia,et al.  The northward spread of leishmaniasis in Italy: evidence from retrospective and ongoing studies on the canine reservoir and phlebotomine vectors , 2008, Tropical medicine & international health : TM & IH.

[18]  A. Tatem,et al.  Global Data for Ecology and Epidemiology: A Novel Algorithm for Temporal Fourier Processing MODIS Data , 2008, PloS one.

[19]  R Moyeed,et al.  Bayesian geostatistical prediction of the intensity of infection with Schistosoma mansoni in East Africa , 2006, Parasitology.

[20]  F. Steurer,et al.  Canine Visceral Leishmaniasis, United States and Canada, 2000–2003 , 2006, Emerging infectious diseases.

[21]  B. Everitt,et al.  A Handbook of Statistical Analyses using R , 2006 .

[22]  A. Trees,et al.  Systematic review of the distribution of the major vector-borne parasitic infections in dogs and cats in Europe , 2003, Veterinary Record.

[23]  P. Bastien,et al.  Value of two PCR methods for the diagnosis of canine visceral leishmaniasis and the detection of asymptomatic carriers , 2002, Parasitology.

[24]  M. Hulme,et al.  A high-resolution data set of surface climate over global land areas , 2002 .

[25]  R. Tibshirani,et al.  Diagnosis of multiple cancer types by shrunken centroids of gene expression , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[26]  S. Hay,et al.  Tools from ecology: useful for evaluating infection risk models? , 2002, Trends in parasitology.

[27]  S I Hay,et al.  Predicting the distribution of urinary schistosomiasis in Tanzania using satellite sensor data , 2001, Tropical medicine & international health : TM & IH.

[28]  M. Wells A Handbook of Statistical Analyses Using Stata , 2001 .

[29]  J. Cox,et al.  Early effects of climate change: do they include changes in vector-borne disease? , 2000, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[30]  P Royston,et al.  The use of fractional polynomials to model continuous risk variables in epidemiology. , 1999, International journal of epidemiology.

[31]  M. Thomson,et al.  Towards a kala azar risk map for Sudan: mapping the potential distribution of Phlebotomus orientalis using digital data of environmental variables , 1999, Tropical medicine & international health : TM & IH.

[32]  D. Hosmer,et al.  Applied Logistic Regression , 1991 .

[33]  Trevor Hastie,et al.  Model Assessment and Selection , 2009 .

[34]  P. Ready Leishmaniasis emergence and climate change. , 2008, Revue scientifique et technique.

[35]  D J Rogers,et al.  Climate change and vector-borne diseases. , 2006, Advances in parasitology.

[36]  Simon I. Hay,et al.  Global mapping of infectious diseases : methods, examples and emerging applications , 2006 .

[37]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[38]  T. Živičnjak,et al.  Canine visceral leishmaniasis , 2000 .

[39]  S. Hay,et al.  Predicting the distribution of tsetse flies in West Africa using temporal Fourier processed meteorological satellite data. , 1996, Annals of tropical medicine and parasitology.

[40]  W. Rogers Regression standard errors in clustered samples , 1994 .

[41]  D. Jarry,et al.  [Ecology of leishmaniasis in Southern France. 18. Enzymatic identification of Leishmania infantum Nicolle, 1908, isolated from Phlebotomus ariasi Tonnoir, 1921, spontaneously infected in the Cévennes]. , 1984, Annales de parasitologie humaine et comparee.

[42]  J. Rioux,et al.  [Ecology of leishmaniasis in southern France. 9. Sampling methods in the study and analysis of canine enzootic leishmaniasis]. , 1978, Annales de parasitologie humaine et comparee.

[43]  J. Rioux,et al.  Écologie des Leishmanioses dans le sud de la France - 9. Les méthodes d’échantillonnage dans le dépistage et l’analyse de l’enzootie canine , 1978 .