Asymmetrically Interacting Spreading Dynamics on Complex Layered Networks
This paper investigates the dynamics of two critical types of spreading processes — epidemic and information diffusion — on complex layered networks, and analyzes their asymmetrical interaction. The research focuses on two principal metrics: the epidemic threshold and the final infection ratio. The framework models the physical contact network for disease spread and the communication network for information dissemination as two distinct layers within a multiplex network.
Key Findings and Analysis
The paper identifies that an epidemic outbreak on a contact layer can simultaneously trigger an information outbreak on the communication layer. This is significant as it indicates that strategic dissemination of information can potentially raise the epidemic threshold, hence making the contact layer more resistant to an outbreak. This effect is more pronounced when there is structural correlation between the communication and contact layers. Interestingly, while the epidemic threshold is enhanced with layered correlations, the information threshold remains unchanged.
A physical theory is constructed to describe the interplay between the two types of spreading dynamics. Two contrasting mechanisms initiate an information outbreak: the intrinsic spreading within the communication layer or the spillover from an epidemic outbreak on the contact layer. Notably, if the intrinsic epidemic spreading model alone suffices to cause an outbreak, the information threshold is effectively zero. Conversely, if information propagates adequately within its layer, it serves as a barrier, restraining the epidemic from spreading by transforming nodes into non-susceptible states through vaccination.
The paper employs heterogeneous mean-field theory to derive the thresholds for both information and epidemic processes on uncorrelated double-layer networks. The authors observe that when information spreading is rapid compared to an epidemic, it significantly alters the network's resilience by enlarging the epidemic threshold. Simulation tests confirm these theoretical predictions and show good alignment with simulated outcomes over various network sizes and structural parameters.
Practical and Theoretical Implications
The implications of this work are profound in epidemiology and network science. On a practical level, the insights can inform public health strategies on leveraging communication networks to disseminate preventive information, potentially curbing an epidemic's reach. Theoretically, this paper advances our understanding of multiplex network dynamics, particularly the nuanced interdependencies that can arise within layered structures.
Inter-layer correlation plays a crucial role in the dynamics. When such correlation is high, nodes with large degrees are likely to be informed faster during an outbreak, enhancing vaccination rates and thereby diminishing the potential for an epidemic. This supports targeting high-centrality nodes in communication campaigns during an impending epidemic.
Future Directions
Future work should consider more complex scenarios involving time-varying network connections, more realistic models integrating human behavioral responses, and the impact of misinformation or competing information. Further studies on the interplay between different types of networks or policies that can affect multiple types of spreading processes concurrently can provide deeper insights into managing real-world contagions on complex networks. The paper opens pathways for incorporating behavioral and information-centric approaches into traditional epidemic models, providing a richer toolkit for tackling emerging challenges in public health.