Coupled propagation between one communicable disease and related two types of information on multiplex networks with simplicial complexes

Currently, regarding the coupling transmission of information and disease, most researches only focus on the impact of one type of information on disease transmission, and some works may be correlated with two types of information, but they are usually based upon pairwise interactions, which ignored the complexity and diversity of information transmission. In real life, the characteristics of interpersonal networks often involve both group interactions and pairwise interactions. Aiming to explore the influence of active or passive information diffusion on the transmission of epidemics on a higher-order network, the coupled two-layered multiplex network model between communicable disease and information transmission is presented. Here, the upper layer characterizes the information transmission network constructed from random simplicial complexes, while the lower layer provides the underlying network that can transmit communicable diseases. First, on the basis of micro Markov chain (MMC) method, the epidemic threshold is analytically derived for the proposed model, indicating that the diffusion of positive and negative preventive information can exert a substantial influence on the critical threshold. Then, by comparing the results acquired by MMC and Monte Carlo simulations (MCS), it can be explicitly expressed that the differences between them are minimal, which implies that the model can predict the transmission of epidemics in the network well. Finally, through extensive MCS, it is also indicated that introducing a 2-simplex into the information layer helps to suppress the propagation or outbreak of communicable diseases, which offers some new ideas or routes for formulating strategies to inhibit communicable diseases.