Our method is inspired by previous works based on the comparison of stochastic dynamic models of the disease spread within host populations to the data [14, [16] [17] [18] [19] [20] . The idea is that each combination of population risk factors leads to a different model. Our objective is to determine the model (i.e. the combination of risk factors) that best fits the data. Beyond the simple analysis of the risk factors associated with FIV, this work aims to understand why the variability observed in our disease prevalence data is so large -data on disease prevalence in males exhibited significant extra-Binomial variations. Can we isolate population risk factors that would explain particularly high disease prevalence in some populations? Does the spatial aggregation of populations with high virus prevalence help to explain the variability in disease prevalence? Or, in contrast, is the large variability observed in disease prevalence a natural consequence of the transmissible nature of the virus?