Effects of awareness diffusion and self-initiated awareness behavior on epidemic spreading - An approach based on multiplex networks

Abstract

a b s t r a c tIn this paper, we study the interplay between the epidemic spreading and the diffusion of awareness in multiplex networks. In the model, an infectious disease can spread in one network representing the paths of epidemic spreading (contact network), leading to the diffusion of awareness in the other network (information network), and then the diffusion of awareness will cause individuals to take social distances, which in turn affects the epidemic spreading. As for the diffusion of awareness, we assume that, on the one hand, individuals can be informed by other aware neighbors in information network, on the other hand, the susceptible individuals can be self-awareness induced by the infected neighbors in the contact networks (local information) or mass media (global information). Through Markov chain approach and numerical computations, we find that the density of infected individuals and the epidemic threshold can be affected by the structures of the two networks and the effective transmission rate of the awareness. However, we prove that though the introduction of the self-awareness can lower the density of infection, which cannot increase the epidemic threshold no matter of the local information or global information. Our finding is remarkably different to many previous results on single-layer network: local information based behavioral response can alter the epidemic threshold. Furthermore, our results indicate that the nodes with more neighbors (hub nodes) in information networks are easier to be informed, as a result, their risk of infection in contact networks can be effectively reduced.

Coupled disease–behavior dynamics on complex networks: A review

“…These findings suggest a new way of understanding realistic contagions and their prevention. Except for obtaining awareness from aware neighbors, self-awareness induced by infected neighbors is another scenario that currently attracts research attention [113], where it is found that coupling such a dynamical process with disease spreading can lower the density of infection, but does not increase the epidemic threshold regardless of the information source.…”

“…Example Refs social network ER network SF network multilayer networks awareness changing disease outbreak or (and) individual prevention measures [79,78,80,82,110,81,111,112,113] square lattice random network SF network urban network learning rules deciding disease status [85,84,93,117] square lattice ER network SF network adaptive network multilayer networks social network social/self-motivated protection mechanisms impacting disease status or (and) topology structure [124,79,130,128,125,126,129,133,83,87,89,91,115,116,122,127,132] time-varying network independent evolution of both disease and behavior processes [138,139] adaptive network multilayer networks social network topology properties determining disease status or (and) individual behavior [93,117,118,131,134,141,142] connection and dynamics process evolve according to their respective rules [135,136,137]. For example, Summin et al recently explored how to lower the number of vaccin...…”

Multistrain edge‐based compartmental model on networks

“…Nevertheless, regarding many infectious diseases, such as TB, hepatitis C virus (HCV), influenza, and bacterial pneumonia, their pathogens have multiple forms named as different strains. Recent researches show that numerous infectious diseases are caused by multistrain pathogen . There are many forms of interrelationship between strains.…”

Impacts of TV and radio advertisements on the dynamics of an infectious disease: A modeling study

“…Over the past few decades, several mathematical studies have been done to assess the effects of media campaigns for the control of infectious diseases . In previous studies, it is assumed that the number of awareness programs is implemented proportional to the number of infected individuals by considering media as a dynamic variable or transmission rate as decreasing function of number of infected individuals due to media alerts.…”