Coupled simultaneous evolution of disease and information on multiplex networks

We analyzed the interaction processes between disease and disease-based information on multiplex networks. Unlike previous models, an epidemic can spread in parallel with the information diffusion during a time step. We used the probability theory to derive a novel individual-based transmission model, which is different from the reported methods, and developed a pairwise formulation of this model. Interestingly, when the infection rate is enough large, the infection curve firstly achieves a large peak, then rapidly decreases to a steady state. In addition, we considered the other two coexistence mechanisms of disease and information during a time step, and found that all mechanisms have the same condition of epidemic outbreak. The theoretical results strongly agree with discrete-time stochastic simulations, and may provide insights for a wide application range for multi-dynamic spreading processes.