Managing epidemics requires to investigate potential impact of risk and protective factors on epidemiological links. Here we focus on links defined by inferred probabilities (transmission links in Equine Influenza, similarity measures of COVID-19 dynamics between different countries). The specific nature of these epidemiological data (zero-inflated, correlated, continuous and bounded) does not allow to use classical supervised methods like linear regression or decision tree to identify impacting factors on the response variable. In this article we propose a by block-permutation-based methodology (i) to identify factors (discrete or continuous) that are potentially significant, (ii) to define a performance indicator to quantify the percentage of correlation explained by the significant factors subset. The methodology is illustrated on simulated data and on the above-mentioned epidemics.