Source-linked AI summary
Dynamical interplay between awareness and epidemic spreading in multiplex networks
Clara Granell, Sergio Gomez, Alex Arenas
TL;DR
The paper studies how cyclic awareness spreading interacts with SIS epidemics across multiplex networks, addressing how awareness affects epidemic onset and incidence. It applies MMCA to the coupled dynamics and finds a meta-critical point beyond which awareness delays and contains the epidemic, while matching Monte Carlo results with low error.
Problem
The paper examines how awareness diffusing through virtual contacts affects epidemic spreading through physical contacts in multiplex networks, because their interplay influences outbreak incidence.
Method
The paper uses a Microscopic Markov Chain Approach to analyze coupled UAU awareness and SIS epidemic processes on quenched two-layer multiplex networks.
Results
The analysis identifies a meta-critical point after which awareness delays and contains epidemic onset, while MMCA predicts epidemic threshold and incidence with errors up to 2.5%.
Takeaways & Limitations
Awareness dynamics and virtual-layer topology can control epidemic onset and reduce epidemic incidence in the modeled multiplex setting.
Takeaways & Limitations
The setup excludes media effects on vaccination campaigns and assumes the specified UAU-SIS dynamics, including complete immunity for aware infected nodes in the analyzed case.
Abstract
from arXiv · showhide
We present the analysis of the interrelation between two processes accounting for the spreading of an epidemics, and the information awareness to prevent its infection, on top of multiplex networks. This scenario is representative of an epidemic process spreading on a network of persistent real contacts, and a cyclic information awareness process diffusing in the network of virtual social contacts between the same individuals. The topology corresponds to a multiplex network where two diffusive processes are interacting affecting each other. The analysis using a Microscopic Markov Chain Approach (MMCA) reveals the phase diagram of the incidence of the epidemics and allows to capture the evolution of the epidemic threshold depending on the topological structure of the multiplex and the interrelation with the awareness process. Interestingly, the critical point for the onset of the epidemics has a critical value (meta-critical point) defined by the awareness dynamics and the topology of the virtual network, from which the onset increases and the epidemics incidence decreases.
Supplemental material
The supplemental figures test MMCA against Monte Carlo across multiplex topologies and parameter spaces, and examine epidemic-threshold dependence on awareness. These comparisons assess approximation accuracy and the predicted phase behavior.
- Epidemic-threshold dependence: βc is plotted against λ for different recovery values, with the shaded region locating possible meta-critical points between 1/Λmax(A) and 1/Λmax(B).The plotted multiplex uses a scale-free physical layer and a virtual layer formed by the same scale-free network plus 400 non-overlapping random links.
- Monte Carlo–MMCA comparisons: The figures compare Monte Carlo and MMCA predictions for the stationary infected fraction ρI across full 100 × 100 λ−β phase diagrams.The tested multiplexes combine scale-free and Erdős-Rényi virtual and physical layers.
- Monte Carlo–MMCA comparisons: 0.9%, 1.0%, and 1.2% are the reported relative errors for the Erdős-Rényi virtual and scale-free physical multiplex.The same phase-diagram comparison uses 50 simulations and a 20% initial infected fraction.
- Monte Carlo–MMCA comparisons: 0.4%, 0.4%, and 0.4% are the reported relative errors for the scale-free virtual and Erdős-Rényi physical multiplex.The comparison uses 50 Monte Carlo simulations with an initial infected fraction of 20%.