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Background: Recently developed techniques to study the spread of infectiousdiseases through networks make assumptions that the initial proportion infectedis infinitesimal and the population behavior is static throughout the epidemic.The models do not apply if the initial proportion is large (and fail wheneverR_0<1), and cannot measure the impact of an intervention.  Methods: In this paper we adapt "edge-based compartmental models" tosituations having finite-sized initial conditions.  Results: The resulting models remain simple and accurately capture the effectof the initial conditions. It is possible to generalize the model to networkswhose partnerships change in time.  Conclusions: The resulting models can be applied to a range of importantcontexts. The models can be used to choose between different interventions thataffect the disease or the population structure.

Epidemics on networks with large initial conditions or changing structure