Effects of the information-driven awareness on epidemic spreading on multiplex networks

Abstract

In this study, we examine the impact of information-driven awareness on the spread of an epidemic from the perspective of resource allocation by comprehensively considering a series of realistic scenarios. A coupled awareness-resource-epidemic model on top of multiplex networks is proposed, and a Microscopic Markov Chain Approach is adopted to study the complex interplay among the processes. Through theoretical analysis, the infection density of the epidemic is predicted precisely, and an approximate epidemic threshold is derived. Combining both numerical calculations and extensive Monte Carlo simulations, the following conclusions are obtained. First, during a pandemic, the more active the resource support between individuals, the more effectively the disease can be controlled; that is, there is a smaller infection density and a larger epidemic threshold. Second, the disease can be better suppressed when individuals with small degrees are preferentially protected. In addition, there is a critical parameter of contact preference at which the effectiveness of disease control is the worst. Third, the inter-layer degree correlation has a “double-edged sword” effect on spreading dynamics. In other words, when there is a relatively lower infection rate, the epidemic threshold can be raised by increasing the positive correlation. By contrast, the infection density can be reduced by increasing the negative correlation. Finally, the infection density decreases when raising the relative weight of the global information, which indicates that global information about the epidemic state is more efficient for disease control than local information.

Impact of Dynamic Infection Rate and Self-Isolation Awareness onNetworked Epidemic Propagation

“…The hotspots of epidemic dynamics at the current stage of work are primarily the structure of interactions between individuals, contact patterns, and migration patterns of populations [22,23]. Granell et al studied the impact of information diffusion on epidemic spreading by establishing a multiplex network [24,25], while Ruan et al extended this investigation by establishing a connection between information diffusion and vaccination, aiming to analyze the dynamics of epidemiology [26]. Since then, more and more realistic characteristics have been introduced into the study of models.…”

Optimizing the robustness of higher-low order coupled networks

“…Malfunctions in these networks can have significant impacts on social life. Therefore, understanding the causes of these malfunctions has become a research focus in network science [2][3][4]. In recent years, the development of network science has led to the proposal of many network models that have important theoretical value [5][6][7][8][9][10][11].…”

A Multi-Information Spreading Model for One-Time Retweet Information in Complex Networks

“…However, given the vast array of information types and complex interactions within online social networks, which significantly impact the dynamics of information diffusion, scholars have increasingly turned their attention to multi-information spreading models. These models aimed to simulate and comprehend the intricate mechanisms underlying information propagation in online social networks [15][16][17][18][19][20][21][22].…”