Logistic regression models are still widely used for the analysis of risk factors associated with infectious diseases, even though their over-simplified independence hypothesis is largely recognised as a limitation to their use [1, 14, 15] . The main difference between the two approaches, based on binomial and dynamic models, comes from the variability expected by their respective H 0 models, as illustrated in Fig. 3a . Binomial models predict much narrower distributions than dynamic models. The consequence is illustrated in Fig. 4 , where we can see that the simple SI model accounts for the observed variability in FIV prevalence in males for a wide range of parameters. In contrast, the binomial test on the distribution of the infected cats among the 15 populations rejects the global binomial distribution hypothesis (p<0.006). To explain the data with a logistic regression model that assumes binomial distributions, additional risk factors need to be invoked. With dynamic models, risk factors are not required to explain the variability in the male disease prevalence observed here. The implication of that is that bringing evidence for population risk factors in infectious disease requires large sample sizes. In our present case n = 499 is not large enough and further sampling is required to bring evidence of population risk factors for FIV transmission.