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A SIRD epidemic model with community structure.

Chaos (Woodbury, N.Y.)ยท2021
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Related Experiment Video

Updated: Jan 4, 2026

Monitoring Spatial Segregation in Surface Colonizing Microbial Populations
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Epidemic spreading on multilayer homogeneous evolving networks.

Jin-Xuan Yang1

  • 1School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming 650221, People's Republic of China.

Chaos (Woodbury, N.Y.)
|November 3, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new model for epidemic spreading on evolving multilayer networks. Increasing a single network layer's epidemic threshold effectively mitigates disease spread, offering a practical control strategy.

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Area of Science:

  • Network Science
  • Epidemiology
  • Complex Systems Dynamics

Background:

  • Multilayer networks are crucial for understanding complex system dynamics.
  • Epidemic spreading on these networks is a significant research area.
  • Existing models lack analysis of disease spread in evolving homogeneous multilayer networks.

Purpose of the Study:

  • To propose a novel epidemic spreading dynamic model for homogeneous evolving networks.
  • To analyze and simulate disease spread dynamics on these evolving networks.
  • To determine the global epidemic threshold and identify control strategies.

Main Methods:

  • Development of a new epidemic spreading model for homogeneous evolving networks.
  • Determination of the global epidemic threshold.
  • Numerical simulations on small-world and random network models.

Main Results:

  • Increasing a single network layer's epidemic threshold aids in mitigating epidemic spread.
  • Initial average network degree and evolutionary parameters influence the epidemic threshold and spread dynamics.
  • A method for calculating falling and rising threshold zones was developed.

Conclusions:

  • The proposed model offers a viable strategy for controlling epidemic spreading.
  • Modifying the threshold of individual network layers is a more feasible control approach than altering all layers simultaneously.
  • Findings are supported by simulations on small-world and random networks.