  can evolve simultaneously. For the information-driven adaptive process,
susceptible (infected) individuals who have abilities to recognize the disease
would break the links of their infected (susceptible) neighbors to prevent the
epidemic from further spreading. Simulation results and numerical analyses
based on the pairwise approach indicate that the information-driven adaptive
process can not only slow down the speed of epidemic spreading, but can also
diminish the epidemic prevalence at the final state significantly. In addition,
the disease spreading and information diffusion pattern on the lattice give a
visual representation about how the disease is trapped into an isolated field
with the information-driven adaptive process. Furthermore, we perform the local
bifurcation analysis on four types of dynamical regions, including healthy,
oscillatory, bistable and endemic, to understand the evolution of the observed
dynamical behaviors. This work may shed some lights on understanding how
information affects human activities on responding to epidemic spreading.
