Risk Estimate of Diseases in Scale-Free Networks

We investigate the effect of risk estimate on the spread of diseases by the standard susceptible--infected--susceptible (SIS) model. The perception of the
risk of being infected is explained as cutting off links among individuals, either healthy or infected. We study this simple dynamics on scale-free networks by analytical methods and computer simulations. We obtain the self-consistency form for the infection prevalence in steady states. For a given transmission rate, there exists a linear relationship between the reciprocal of the density of
infected nodes and the estimate parameter. We confirm all the results by sufficient numerical simulations.