Self-awareness control effect of cooperative epidemics on complex networks

Recursive traffic percolation on urban transportation systems

This paper proposes a recursive traffic percolation framework to capture the dynamics of cascading failures and analyze potential overloaded bottlenecks. In particular, compared to current work, the influence of external flow is considered, providing a new perspective for the study of regional commuting. Finally, we present an empirical study to verify the accuracy and effectiveness of our framework. Further analysis indicates that external flows from different regions affect the network. Our work requires only primary data and verifies the improvement of the functional network.

Effects of measures on phase transitions in two cooperative susceptible-infectious-recovered dynamics

In recent studies, it has been shown that a cooperative interaction in a co-infection spread can lead to a discontinuous transition at a decreased threshold. Here, we investigate the effects of immunization with a rate proportional to the extent of the infection on phase transitions of a cooperative co-infection. We use the mean-field approximation to illustrate how measures that remove a portion of the susceptible compartment, like vaccination, with high enough rates can change discontinuous transitions in two coupled susceptible-infectious-recovered dynamics into continuous ones while increasing the threshold of transitions. First, we introduce vaccination with a fixed rate into a symmetric spread of two diseases and investigate the numerical results. Second, we set the rate of measures proportional to the size of the infectious compartment and scrutinize the dynamics. We solve the equations numerically and analytically and probe the transitions for a wide range of parameters. We also determine transition points from the analytical solutions. Third, we adopt a heterogeneous mean-field approach to include heterogeneity and asymmetry in the dynamics and see if the results corresponding to homogeneous symmetric case stand.

Spreading of two interacting diseases in multiplex networks

We consider the interacting processes between two diseases on multiplex networks, where each node can be infected by two interacting diseases with general interacting schemes. A discrete-time individual-based probability model is rigorously derived. By the bifurcation analysis of the equilibrium, we analyze the outbreak condition of one disease. The theoretical predictions are in good agreement with discrete-time stochastic simulations on scale-free networks. Furthermore, we discuss the influence of network overlap and dynamical parameters on the epidemic dynamical behaviors. The simulation results show that the network overlap has almost no effect on both epidemic threshold and prevalence. We also find that the epidemic threshold of one disease does not depend on all system parameters. Our method offers an analytical framework for the spreading dynamics of multiple processes in multiplex networks.