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Related Experiment Video

Updated: May 24, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Epidemic threshold and control in a dynamic network.

Michael Taylor1, Timothy J Taylor, Istvan Z Kiss

  • 1School of Mathematical and Physical Sciences, Department of Mathematics, University of Sussex, Brighton UK-BN1 9QH, England, United Kingdom. mt264@sussex.ac.uk

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|March 10, 2012
PubMed
Summary
This summary is machine-generated.

This study models epidemics on dynamic contact networks, finding that compartmental models accurately predict disease spread and thresholds. The model captures network evolution effects on epidemic dynamics.

Related Experiment Videos

Last Updated: May 24, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

  • Epidemiology
  • Network Science
  • Mathematical Biology

Background:

  • Epidemics spread through contact networks.
  • Dynamic networks, with changing links, complicate epidemic modeling.
  • Existing models may not fully capture evolving network structures.

Purpose of the Study:

  • To develop and validate a compartmental model for susceptible-infected-susceptible (SIS) epidemics on dynamic contact networks.
  • To analyze epidemic thresholds and dynamics in relation to network evolution.
  • To compare model predictions with stochastic network simulations.

Main Methods:

  • Adaptation of an effective degree compartmental modeling framework.
  • Numerical solution of ordinary differential equations (ODEs).
  • Comparison with individual-based stochastic network simulations.
  • Analytical calculation of the basic reproduction number (R0).

Main Results:

  • The ODE model shows excellent agreement with stochastic simulations for disease and network evolution.
  • The model accurately captures epidemic thresholds across various parameters.
  • Analytical R0 calculations reveal limiting cases of static networks and rapid mixing.
  • Local stability analysis can be misleading for evolving networks.

Conclusions:

  • Compartmental models are effective for studying epidemics on dynamic networks.
  • Network evolution significantly influences epidemic dynamics and thresholds.
  • Accurate modeling requires considering the interplay between network and disease timescales.