Epidemiological models become more and more complex as new concerns are taken into account (age, sex, spatial heterogeneity, containment or vaccination policies, etc.). This is problematic because these aspects are typically intertwined which makes models difficult to extend, change or reuse. The Kendrick approach has shown promising results to separate epidemiological concerns but is restricted to homogeneous compartmental models. In this paper, we report on an attempt to generalize the Kendrick approach to support some aspects of contact networks, thereby improving the predictive quality of models with significant heterogeneity in the structure of contacts, while keeping the simplicity of compartmental models. This approach has been validated on two different techniques to generalize compartmental models.