Epidemic spreading on time-varying multiplex networks

TL;DR

This paper introduces a model for epidemic spreading on time-varying multiplex networks, revealing how layer overlap and temporal activation patterns influence contagion thresholds and dynamics.

Contribution

It provides a novel analytical and numerical framework to study epidemic thresholds considering the interplay of multiplexity and temporal dynamics.

Findings

Layer overlap reduces epidemic threshold, especially with positive correlation in node activation.

In networks with uneven layer connectivity, the denser layer dominates contagion dynamics.

Overlap impacts spreading in less connected layers but not in the densest layer.

Abstract

Social interactions are stratified in multiple contexts and are subject to complex temporal dynamics. The systematic study of these two features of social systems has started only very recently mainly thanks to the development of multiplex and time-varying networks. However, these two advancements have progressed almost in parallel with very little overlap. Thus, the interplay between multiplexity and the temporal nature of connectivity patterns is poorly understood. Here, we aim to tackle this limitation by introducing a time-varying model of multiplex networks. We are interested in characterizing how these two properties affect contagion processes. To this end, we study SIS epidemic models unfolding at comparable time-scale respect to the evolution of the multiplex network. We study both analytically and numerically the epidemic threshold as a function of the overlap between, and the features of, each layer. We found that, the overlap between layers significantly reduces the epidemic threshold especially when the temporal activation patterns of overlapping nodes are positively correlated. Furthermore, when the average connectivity across layers is very different, the contagion dynamics are driven by the features of the more densely connected layer. Here, the epidemic threshold is equivalent to that of a single layered graph and the impact of the disease, in the layer driving the contagion, is independent of the overlap. However, this is not the case in the other layers where the spreading dynamics are sharply influenced by it. The results presented provide another step towards the characterization of the properties of real networks and their effects on contagion phenomena