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Figure 1.


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Principal component analysis of variables (PCA). The factor map helps to visualize the cluster of correlated variables in groups (≅90°). Cos2 is the gradient of quality to highlight the most important variables in explaining the variations retained by the principal components. Dimension 1 and 2 (Dim1 and 2) is the space where variables are expressed (<38% of variance). The distribution of the surveyed population through variables is also observed. Variables analyzed: (1) sex, (2) weight, (3) age, (4) number of animal per household, (5) veterinary attention, (6) type of dwelling, (7) access to green areas, (8) drinking water, (9) disease information, (10) prevalence and (11) region.

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