  Background: Recently developed techniques to study the spread of infectious
diseases through networks make assumptions that the initial proportion infected
is infinitesimal and the population behavior is static throughout the epidemic.
The models do not apply if the initial proportion is large (and fail whenever
R_0<1), and cannot measure the impact of an intervention.
  Methods: In this paper we adapt "edge-based compartmental models" to
situations having finite-sized initial conditions.
  Results: The resulting models remain simple and accurately capture the effect
of the initial conditions. It is possible to generalize the model to networks
whose partnerships change in time.
  Conclusions: The resulting models can be applied to a range of important
contexts. The models can be used to choose between different interventions that
affect the disease or the population structure.
