Suppressing disease spreading by using information diffusion on multiplex networks

Abstract

Although there is always an interplay between the dynamics of information diffusion and disease spreading, the empirical research on the systemic coevolution mechanisms connecting these two spreading dynamics is still lacking. Here we investigate the coevolution mechanisms and dynamics between information and disease spreading by utilizing real data and a proposed spreading model on multiplex network. Our empirical analysis finds asymmetrical interactions between the information and disease spreading dynamics. Our results obtained from both the theoretical framework and extensive stochastic numerical simulations suggest that an information outbreak can be triggered in a communication network by its own spreading dynamics or by a disease outbreak on a contact network, but that the disease threshold is not affected by information spreading. Our key finding is that there is an optimal information transmission rate that markedly suppresses the disease spreading. We find that the time evolution of the dynamics in the proposed model qualitatively agrees with the real-world spreading processes at the optimal information transmission rate.

Interlayer link prediction in multiplex social networks: An iterative degree penalty algorithm

“…However, it is difficult to apply such a representation to describe multilayer structures such as multiple OSNs, multiple transportation networks [10] as well as the dynamic networks [7,9]. The multilayer structures have a significant influence on the aspects of cascade [11,12], propagation [13,14,15,16], synchronization [17], and game [18,19,20]. In recent years, the multiplex network [11,21,22] has emerged to characterize these multilayer structures.…”

Statistical physics of vaccination

“…Multiplex networks, as a typical kind of multilayer network structure, can be regarded as the combination of several network layers, which contain the same nodes (or share at least some fraction of nodes) yet different intra-layer connections [203,231,240,241,[338][339][340][341][342][343][344][356][357][358]. For each node, we term its counterpart in every layer as a 'replica'.…”

“…This research provides vital insight in terms of the efficiency of vaccination under different circumstances, yet empirical networks usually have particular topology properties that require special care. This includes taking into account community structure [170,188,[329][330][331][332], changes in the network structure over time [52,218,[333][334][335][336][337], as well as multilayer properties that are typical for a large plethora of real-world networks [203,231,240,241,[338][339][340][341][342][343][344][345][346][347]. In what follows, we will review vaccination programs that take into account the particular aspects of these properties on various types of networks.…”

The Impact of Heterogeneity and Awareness in Modeling Epidemic Spreading on Multiplex Networks

“…Furthermore, recently, the interplay between disease and awareness dynamics22232425 has gained a lot of interest, studying how individuals, aware of the potential spread of a certain disease, are able to take preventive measures protecting themselves. In most of these studies, it is explored the interesting interplay between awareness and epidemics when both phenomena compete using different layers of propagation232627282930. Although a lot of works have exploited the framework of multiplex networks and studied the dynamics of the two spreading processes, awareness and disease, none of them has explored the realistic coevolution of the two processes in all the layers of a multiplex network.…”

“…These estimators allow us evaluating how the various characteristics of the node, also in terms of social categories, can affect the awareness and hence the epidemic dynamics. We compare analytic and simulation results using data observed in a particular temporal window30, to evaluate the coherence of our model fitted with data. The data are referred to Zika virus41, an emerging viral disease representing a present public health threat.…”