Fig 1.
Geospatial location of human cases of VL and the serosurvey in Bauru’s urban area.
A total of 6,578 blood samples of dogs were analyzed. Points represent each dog’s address. Positive dogs for CVL are represented by red dots and negative by blue. Black dots are HVL cases. Points are overlapped because of the spatial resolution of the cartographic scale.
Fig 2.
Synthesis of the performed methodology.
The methodological proceedings were performed according to (a) Collecting samples and guardian’s survey. (b) Analyzing the samples. (c) Mapping the samples of dogs and human cases of VL. (d) Changing the scale of the dogs into households. (e) Applying statistical models. (f) Performing thematic maps. (g) Preparing data for the spatial model. (h) Preparing training data for the spatial model. (i) Validation of the models. (j) Using acquired knowledge for decision-making.
Fig 3.
Geocoded households of Bauru, stratified by dog population, positive dogs, and buffer zones.
The households are identified according to the conducted surveys. Yellow, orange, or red symbols represent the dog’s households sampled. Yellow had not an infected dog; orange had an infected dog (in the past); red had and currently has an infected dog. Proportional circles represent the number of dogs in each household. We created a buffer of 100m in each sampled household to calculate the number of dogs, positive or negative dogs in this area.
Fig 4.
Geocoded households of Bauru, stratified by prevalence and cluster analysis.
Each black dot is a household with no identified cluster. Magenta dots are the clusters of domiciles with infected dogs; green dots are the clusters of households that already had infected dogs; and orange dots represent the clusters of households that had and currently have infected dogs. Different size symbols and opacity households were set to ensure the spatial visualization of overlapped households.
Fig 5.
Kernel density ratio for canine visceral leishmaniasis.
Kernel density ratio map ranging from 0 (blue) to 0.7 (red), which gives a visualization of the risk dividing the concentration of cases of CVL (S3 Fig) by the concentration of dog samples (S4 Fig). The areas of higher risk are in the west and southwest.
Table 1.
CVL diagnostic, dog count in the households, and buffer zone extraction versus the number of sampled dogs in Bauru.
Table 2.
Binary logistic regression of canine visceral leishmaniasis diagnostic, dog count in the buffer zone extraction versus the number of positive dogs and human cases in Bauru.
Fig 6.
Predicting the risk for canine visceral leishmaniasis using geospatial methods.
Spatial prediction models using the dog population. Risk is scaled from low (blue) to high (red), as shown by the legends. (a) Geostatistical approach using the ordinary Kriging method. (b) Generalized additive model.
Fig 7.
Area under the receiver operating characteristic (ROC) for canine visceral leishmaniasis models.
For each model, the AUC was calculated with 95% confidence intervals. The best model in predicting canine risk disease was the Kernel density ratio map.