Epidemic spreading and immunization strategy in multiplex networks

TL;DR

This paper models epidemic spread in partially overlapping multiplex networks, proposing a random immunization strategy and analyzing its effectiveness through theoretical and simulation methods, revealing a lowered epidemic threshold.

Contribution

It introduces a novel framework for epidemic modeling in partially overlapped multiplex networks and evaluates a new immunization strategy's impact.

Findings

Immunization reduces epidemic spread in the network.

Overlapping fraction influences disease propagation and control.

Theoretical and simulation results agree on lowered epidemic threshold.

Abstract

A more connected world has brought major consequences such as facilitate the spread of diseases all over the world to quickly become epidemics, reason why researchers are concentrated in modeling the propagation of epidemics and outbreaks in Multilayer Networks. In this networks all nodes interact in different layers with different type of links. However, in many scenarios such as in the society, a Multiplex Network framework is not completely suitable since not all individuals participate in all layers. In this paper, we use a partially overlapped Multiplex Network where only a fraction of the individuals are shared by the layers. We develop a mitigation strategy for stopping a disease propagation, considering the Susceptible-Infected-Recover model, in a system consisted by two layers. We consider a random immunization in one of the layers and study the effect of the overlapping fraction in both, the propagation of the disease and the immunization strategy. Using branching theory, we study this scenario theoretically and via simulations and find a lower epidemic threshold than in the case without strategy.