  Adaptive networks have been recently introduced in the context of disease
propagation on complex networks. They account for the mutual interaction
between the network topology and the states of the nodes. Until now, existing
models have been analyzed using low-complexity analytic formalisms, revealing
nevertheless some novel dynamical features. However, current methods have
failed to reproduce with accuracy the simultaneous time evolution of the
disease and the underlying network topology. In the framework of the adaptive
SIS model of Gross et al. [Phys. Rev. Lett. 96, 208701 (2006)], we introduce an
improved compartmental formalism able to handle this coevolutionary task
successfully. With this approach, we analyze the interplay and outcomes of both
dynamical elements, process and structure, on adaptive networks featuring
different degree distributions at the initial stage.
