  We investigate the dynamics of an epidemiological
susceptible-infected-susceptible (SIS) model on an adaptive network. This model
combines epidemic spreading (dynamics on the network) with rewiring of network
connections (topological evolution of the network). We propose and implement a
computational approach that enables us to study the dynamics of the network
directly on an emergent, coarse-grained level. The approach sidesteps the
derivation of closed low-dimensional approximations. Our investigations reveal
that global coupling, which enters through the awareness of the population to
the disease, can result in robust large-amplitude oscillations of the state and
topology of the network.
