An SIS epidemic model with distributed infection rate in complex network

We propose a modified Susceptible-Infected-Susceptible (SIS) model to analyze the influence of the distributed infection rate on the epidemic spreading behavior in complex networks. Unlike the traditional SIS model, the spreading rate is assumed to take several typical distributions, such as two-point, uniform and exponential distributions. This kind of distributed spreading rate can characterize the individual´s difference of infectivity which is often observed in many real epidemics. Largescale numerical simulations show that the distributed infection rate can significantly reduce the density of the infected nodes, and slow down the dynamical propagation process. Thus, current results are of high importance for us to further understand the disease propagation properties of epidemics in real complex systems.