Effective degree theory for awareness and epidemic spreading on multiplex networks

Abstract

Epidemic spreading processes on multiplex networks have richer dynamical properties than those on single layered networks. To describe the intertwined processes on such networks, heterogeneous mean field (HMF) approach for continuous-time processes and microscopic Markov chain approach for discrete-time processes have been proposed. However, it has been shown that the time evolution of infected individuals and the final epidemic size obtained from these approaches have noticeable discrepancy comparing to those from Monte Carlo simulations. In this paper, we extend the approach of effective degree theory (EDT) on multiplex networks. We will show that predictions obtained from the EDT have excellent agreement with Monte Carlo simulations. Moreover, since the dynamics on multiplex networks involve more dynamical variables, which may invoke more computations, to reduce the computational burden, we further develop an approach based on partial effective degree theory (PEDT) for analyzing the dynamics on multiplex networks, where one layer adopts EDT and the other layer adopts the HMF. Our results show that PEDT has a good performance in predicting the target dynamical process.

Defending against Online Social Network Rumors through Optimal Control Approach

“…e spread of rumors in the real world may be more complicated, and there are some open problems. First, since there is more than one OSN in the real world [48,49], the rumor-containing problem should be extended to multiplex OSNs. Second, since realistic OSNs are varying over time [50,51], it is necessary to study the rumor-containing problem with dynamic OSNs.…”

Coupling Epidemiological Models with Social Dynamics

“…So, the transitions involve a set of ordinary differential equations modelling the disease dynamics coupled with different social contact processes like the voter model, Axelrod's cultural transmission model (Axelrod 1997), imitation dynamics (Bauch 2005), or other discrete opinion dynamics models like Galam's, Sznajd's, or Ochrombel's (Galam and Zucker 2000;Ochrombel 2001;Slanina and Lavicka 2003;Sznajd-Weron and Sznajd 2000). Now, the possibility of an epidemic outbreak, and its long-term impact, will depend on the size of those groups, the mechanisms of transmission of the disease, the social contact process, and also on the clustering or other properties of the network modelling the social structure of the population, see for instance (Dorso et al 2017;da Silva et al 2019;Su et al 2018;Tyson et al 2020;Zhou et al 2019).…”

Using awareness to Z-control a SEIR model with overexposure: Insights on Covid-19 pandemic

“…Most countries and the World Health Organization (WHO) have used the great potential of Internet to promote awareness and educational programs on Covid19 and surveying the Relative Internet Search Volumes (RSV) was deemed as a means to give information on the extent of public attention, with Google Trends as one of the most widely used tools for this aim [9] , [10] . In addition, a better understanding of the complex potential provided by awareness for the containment of epidemics is a hot spot that continues to attract attention in many different directions, as testified by a multiplicity of studies involving homogeneous spread qualitative models [11] , [12] , [13] , [14] , [15] , networks [16] , [17] , [18] and multilayer or multiplex networks [19] , [20] , [21] , [22] , [23] . In evaluating the impact of awareness on disease spread, network-based models can certainly offer a deeply realistic perspective since they account for the heterogeneous structure of society and for the individuality of the members of a population.…”