Asymmetrical dynamics of epidemic propagation and awareness diffusion in multiplex networks

Abstract

To better explore asymmetrical interaction between epidemic spreading and awareness diffusion in multiplex networks, we distinguish susceptibility and infectivity between aware and unaware individuals, relax the degree of immunization, and take into account three types of generation mechanisms of individual awareness. We use the probability trees to depict the transitions between distinct states for nodes and then write the evolution equation of each state by means of the microscopic Markovian chain approach (MMCA). Based on the MMCA, we theoretically analyze the possible steady states and calculate the critical threshold of epidemics, related to the structure of epidemic networks, the awareness diffusion, and their coupling configuration. The achieved analytical results of the mean-field approach are consistent with those of the numerical Monte Carlo simulations. Through the theoretical analysis and numerical simulations, we find that global awareness can reduce the final scale of infection when the regulatory factor of the global awareness ratio is less than the average degree of the epidemic network but it cannot alter the onset of epidemics. Furthermore, the introduction of self-awareness originating from infected individuals not only reduces the epidemic prevalence but also raises the epidemic threshold, which tells us that it is crucial to enhance the early warning of symptomatic individuals during pandemic outbreaks. These results give us a more comprehensive and deep understanding of the complicated interaction between epidemic transmission and awareness diffusion and also provide some practical and effective recommendations for the prevention and control of epidemics.

Studying the co-evolution of information diffusion, vaccination behavior and disease transmission in multilayer networks with local and global effects

“…In practice, the different information perception abilities of individuals and the different conscious responses of different individuals who are informed of information also have an impact on disease transmission. [22][23][24][25][26] Sun et al [22] studied the asymmetric interaction between disease transmission and consciousness diffusion in multiple networks, distinguishing susceptibility and infectiousness between conscious and unconscious individuals, and the results showed that the introduction of self-awareness in infected individuals increased. Chang et al [25] investigated the effect of the heterogeneity of information layers on disease transmission thresholds, and showed that weak heterogeneity of information layers was more effective in inhibiting disease transmission than strong heterogeneity when the rate of consciousness transmission was within a certain range.…”

The coevolution of the spread of a disease and competing opinions in multiplex networks

“…Guo et al [25] further explored the influence of local awareness in an epidemic transmission model in multiplex networks, and discovered the ratio of local awareness possessed a two-stage cascade of effects on the epidemic threshold and the final epidemic size. Sun et al [26] introduced three types of generation mechanisms of individual awareness in multiplex networks, and indicated that increasing self-awareness among infected individuals not only reduces the prevalence of the epidemic but also increases the epidemic threshold.…”

The impact of the self-recognition ability and physical quality on coupled negative information-behavior-epidemic dynamics in multiplex networks

“…Peng et al [47] proposed a behavior spreading model to describe the heterogeneity of individuals in adopting the behavior, in which the adoption threshold follows the Gaussian distribution. Sun et al [48] distinguished susceptibility and infectivity between aware and unaware individuals, relaxed the degree of immunization, and took into account three types of generation mechanisms of individual awareness. Tian et al [49] considered edge weight distribution and adoption heterogeneity on the networks separately, where the adoption thresholds of nodes obey truncated Gaussian distribution.…”