The impact of nodes of information dissemination on epidemic spreading in dynamic multiplex networks

Abstract

Epidemic spreading processes on dynamic multiplex networks provide a more accurate description of natural spreading processes than those on single layered networks. To describe the influence of different individuals in the awareness layer on epidemic spreading, we propose a two-layer network-based epidemic spreading model, including some individuals who neglect the epidemic, and we explore how individuals with different properties in the awareness layer will affect the spread of epidemics. The two-layer network model is divided into an information transmission layer and a disease spreading layer. Each node in the layer represents an individual with different connections in different layers. Individuals with awareness will be infected with a lower probability compared to unaware individuals, which corresponds to the various epidemic prevention measures in real life. We adopt the micro-Markov chain approach to analytically derive the threshold for the proposed epidemic model, which demonstrates that the awareness layer affects the threshold of disease spreading. We then explore how individuals with different properties would affect the disease spreading process through extensive Monte Carlo numerical simulations. We find that individuals with high centrality in the awareness layer would significantly inhibit the transmission of infectious diseases. Additionally, we propose conjectures and explanations for the approximately linear effect of individuals with low centrality in the awareness layer on the number of infected individuals.

Dynamics of information diffusion and disease transmission in time-varying multiplex networks with asymmetric activity levels

“…This type of information prompts individuals to take self-protection measures, such as social distancing and vaccinations, thereby exerting a significant influence on disease transmission. [25][26][27][28][29][30][31] Researchers have increasingly focused on the relationship between information and disease, and have achieved gratifying results. For instance, Funk et al investigated the influence of information on dynamic disease transmission, revealing that information can regulate the magnitude of infectious disease outbreak.…”

Stochastic dynamical analysis for the complex infectious disease model driven by multisource noises

“…Infectious diseases can be transmitted between people, animals and goods which always threaten human survival and take great challenges to whole world [ 1 – 3 ]. In recent decades, a large number of mathematical models for infectious diseases have been built and studied widely to realize the infectious disease.…”

Effects of behavioral observability and social proof on the coupled epidemic-awareness dynamics in multiplex networks

“…They concluded that these two kinds of information have contrary impacts on epidemic prevention and control. Additionally, Feng et al [ 28 ] uncovered how individuals with different properties in the information diffusion layer influences the transmission of epidemics. These investigations suggest that epidemic spreading is highly related to the relevant information diffusion.…”