The spread of a transmissible disease in a host population is a dynamic process where the probability of individuals becoming infected depends on the number of infected individuals in their neighborhood. Nowadays, dynamic models of epidemics are widely accepted as efficient tools to help understand the spread and management of infectious diseases (see e.g. [32] [33] [34] [35] ). So it is not surprising that stochastic versions of these models have emerged during the past decade as the best way to analyze infectious diseases data (see e.g. [1, [14] [15] [16] [17] [18] [19] 20, 36, 37] ). Methods based on the comparison of stochastic epidemic models to data hence constitute natural tools to estimate how different factors may affect the spread and impact of infectious diseases.