A coupled awareness—epidemic model on a multi-layer time-varying network

Abstract

Social interactions have become more complicated and changeable under the influence of information technology revolution. We, thereby, propose a multi-layer activity-driven network with attractiveness considering the heterogeneity of activated individual edge numbers, which aims to explore the role of heterogeneous behaviors in the time-varying network. Specifically, three types of individual behaviors are introduced: (i) self-quarantine of infected individuals, (ii) safe social distancing between infected and susceptible individuals, and (iii) information spreading of aware individuals. Epidemic threshold is theoretically derived in terms of the microscopic Markov chain approach and the mean-field approach. The results demonstrate that performing self-quarantine and maintaining safe social distance can effectively raise the epidemic threshold and suppress the spread of diseases. Interestingly, individuals’ activity and individuals’ attractiveness have an equivalent effect on epidemic threshold under the same condition. In addition, a similar result can be obtained regardless of the activated individual edge numbers. The epidemic outbreak earlier in a situation of the stronger heterogeneity of activated individual edge numbers.

Impact of asymmetric activity on interactions between information diffusion and disease transmission in multiplex networks

“…In real life, once people realize the potential threat of disease, they will take effective measures to prevent infection in time, such as wearing masks, vaccination, etc. The awareness is reflected in the contact behavior that would change the connection patterns [24,25]. Therefore, scholars have begun to study the transmission dynamics of information and disease on multiple networks [26].…”

Study on the influence of the information diffusion and vaccination behavior on disease transmission in multiplex networks

“…In fact, when disease outbreak occurs, disease-related information also diffuses among the population, and we learn about the risk of the disease and some preventive measures based on this information as a way to reduce our risk of being infected, which in turn has an important impact on the transmission of the disease [20][21][22][23][24][25]. Funk et al [22] analyzed the law of disease transmission based on the SIR model, and found that the diffusion of information can reduce the scale of the transmission of the disease; Granell et al [23] established UAU-SIS twolayer network model to study the coupled information and disease spreading law, and found that information can effectively inhibit disease transmission at the meta-critical point.…”