Risk perception in epidemic modeling

TL;DR

This paper explores how risk perception influences epidemic spread, demonstrating that adaptive behaviors can halt outbreaks in some networks but require nonlinear responses in others, revealing complex epidemic dynamics.

Contribution

It introduces a model incorporating dynamic risk perception affecting infectivity, highlighting the importance of nonlinear perception in controlling epidemics on complex networks.

Findings

Perception can halt epidemics in homogeneous and random networks.

Linear perception fails to stop epidemics in scale-free networks.

Nonlinear perception can lead to disease extinction, showing a discontinuous transition.

Abstract

We investigate the effects of risk perception in a simple model of epidemic spreading. We assume that the perception of the risk of being infected depends on the fraction of neighbors that are ill. The effect of this factor is to decrease the infectivity, that therefore becomes a dynamical component of the model. We study the problem in the mean-field approximation and by numerical simulations for regular, random and scale-free networks. We show that for homogeneous and random networks, there is always a value of perception that stops the epidemics. In the ``worst-case'' scenario of a scale-free network with diverging input connectivity, a linear perception cannot stop the epidemics; however we show that a non-linear increase of the perception risk may lead to the extinction of the disease. This transition is discontinuous, and is not predicted by the mean-field analysis.