2007/05/31 by Franco Bagnoli, Píetro Lió, Pietro Lio +1 · 158 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · Physics and Astronomy · Psychology · #COVID-19 epidemiological studies #Cartography #Combinatorics #Complex Network Analysis Techniques #Complex network #Computer science #Demography #Econometrics #Epidemic model #Extinction (optical mineralogy) #Fraction (chemistry) #Geography #Homogeneous #Mathematics #Nonlinear system #Opinion Dynamics and Social Influence #Perception #Physics #Population #Psychology #Risk perception #Scale (ratio) #Scale-free network #Statistical physics #Statistics #q-bio.OT #q-bio.PE
paper · pdf · doi:10.1103/physreve.76.061904
published in Physical Review E 76(6), 061904 (American Physical Society) · 6 pages, 6 figures, completely new version
arxiv created 2007/08/23 · openalex publication_date 2007/12/05 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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 nonlinear 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.