Risk perception in epidemic modeling

dc.creatorBagnoli, Franco
dc.creatorLio, Pietro
dc.creatorSguanci, Luca
dc.date2007-05-14
dc.date2007-08-23
dc.date.accessioned2026-07-07T08:47:20Z
dc.date.available2026-07-07T08:47:20Z
dc.descriptionWe 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.
dc.description6 pages, 6 figures, completely new version
dc.identifierhttps://arxiv.org/abs/0705.1974
dc.identifierhttp://arxiv.org/abs/0705.1974
dc.identifierPhys. Rev. E 76, 061904 (2007)
dc.identifierdoi:10.1103/PhysRevE.76.061904
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/143560
dc.subjectPopulations and Evolution
dc.subjectOther Quantitative Biology
dc.titleRisk perception in epidemic modeling
dc.typetext

Files

Collections