Epidemic Model with Isolation in Multilayer Networks

TL;DR

This paper introduces an $SI_IR$ epidemic model incorporating isolation in multilayer networks, showing how isolation reduces disease spread and can prevent epidemics under certain conditions.

Contribution

It develops a novel $SI_IR$ model with an isolation parameter in multilayer networks, analyzing its impact on epidemic thresholds and disease dynamics.

Findings

Isolation increases epidemic threshold

Maximum isolation can prevent epidemics

Traditional SIR overestimates infection risk

Abstract

The Susceptible-Infected-Recovered (SIR) model has successfully mimicked the propagation of such airborne diseases as influenza A (H1N1). Although the SIR model has recently been studied in a multilayer networks configuration, in almost all the research the isolation of infected individuals is disregarded. Hence we focus our study in an epidemic model in a two-layer network, and we use an isolation parameter to measure the effect of isolating infected individuals from both layers during an isolation period. We call this process the Susceptible-Infected-Isolated-Recovered ($SI_IR$) model. The isolation reduces the transmission of the disease because the time in which infection can spread is reduced. In this scenario we find that the epidemic threshold increases with the isolation period and the isolation parameter. When the isolation period is maximum there is a threshold for the isolation parameter above which the disease never becomes an epidemic. We also find that epidemic models, like $SIR$ overestimate the theoretical risk of infection. Finally, our model may provide a foundation for future research to study the temporal evolution of the disease calibrating our model with real data.