Impact of heterogeneous infection rates on traffic-driven epidemic spreading dynamics in complex networks

Although much attention has been focused on traffic-driven epidemic spreading on complex networks, the vast majority of theoretical approaches assumes an identical infection rate for all nodes. This paper investigates the effect of heterogenous infection rates on epidemic spreading, in which the infection rate obeys Binomial distribution. Mean-field approximation and continuous-time Markov chain are adopted to establish the control equation for the model. Results show that heterogeneous infection rates can suppress or promote the epidemic spreading. By adjusting the parameters of infection heterogeneity or the proportion of different nodes, the scope and the threshold of epidemic spreading can be controlled. Simulations on Erdös–Rényi random networks and Barabási–Albert scale-free networks prove the accuracy of theoretical predictions.