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Related Experiment Video

Updated: Jul 7, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Risk perception in epidemic modeling.

Franco Bagnoli1, Pietro Liò, Luca Sguanci

  • 1Department of Energy, University of Florence, Via S. Marta 3, Florence, Italy. Franco.Bagnoli@unifi.it

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|February 1, 2008
PubMed
Summary
This summary is machine-generated.

Risk perception can halt epidemic spreading in most networks. However, in complex scale-free networks, nonlinear risk perception is crucial for disease extinction, a finding missed by mean-field analysis.

Related Experiment Videos

Last Updated: Jul 7, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Epidemiology
  • Network Science
  • Mathematical Modeling

Background:

  • Epidemic spreading models often simplify the role of individual risk perception.
  • Understanding how perceived risk influences disease transmission is vital for public health interventions.
  • Network structure significantly impacts epidemic dynamics.

Purpose of the Study:

  • To investigate the impact of risk perception on epidemic spreading models.
  • To analyze how perceived risk, dependent on infected neighbors, affects infectivity.
  • To explore epidemic control strategies in various network topologies.

Main Methods:

  • Developed a simple epidemic spreading model incorporating dynamical infectivity based on risk perception.
  • Employed mean-field approximation for theoretical analysis.
  • Conducted numerical simulations on regular, random, and scale-free networks.

Main Results:

  • Homogeneous and random networks show epidemic cessation with sufficient risk perception.
  • Scale-free networks with high connectivity pose challenges for linear risk perception.
  • Nonlinear risk perception can induce disease extinction in scale-free networks, a discontinuous transition.

Conclusions:

  • Risk perception is a critical factor in controlling epidemics, with network topology playing a key role.
  • Nonlinear risk perception offers a potential strategy for disease extinction in complex networks.
  • Mean-field approximations may fail to capture crucial epidemic dynamics in heterogeneous networks.