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Updated: Feb 15, 2026
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Peptide-based Identification of Functional Motifs and their Binding Partners
Published on: June 30, 2013
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The epidemic model based on the approximation for third-order motifs on networks.
Jinxian Li1, Weiqiang Li2, Zhen Jin3
1School of Mathematical Sciences, Shanxi University, Taiyuan 030006, PR China; Shanxi Key Laboratory of Mathematical Techniques and Big Data Analysis on Disease Control and Prevention, Taiyuan 030006, PR China.
Mathematical Biosciences
|January 14, 2018
Summary
Network structure impacts infectious disease spread. Higher clustering coefficients in susceptible-infected-recovered (SIR) models impede disease transmission, reducing epidemic size and peak infections.
Area of Science:
- Epidemiology
- Network Science
- Mathematical Modeling
Background:
- Infectious disease transmission is influenced by network structure.
- Existing models often simplify network properties.
- Incorporating network parameters is crucial for accurate epidemic modeling.
Purpose of the Study:
- To develop a novel moment closure epidemic model incorporating network structure.
- To investigate the influence of network motifs and clustering coefficients on disease spread.
- To establish a susceptible-infected-recovered (SIR) model based on approximated third-order motifs.
Main Methods:
- Developed a new moment closure epidemic model using third-order motif approximation.
- Derived ordinary differential equations to describe disease spread.
- Established and analyzed a susceptible-infected-recovered (SIR) model.
- Calculated the basic reproduction number and performed numerical simulations.
Main Results:
- The basic reproduction number decreases with increasing clustering coefficient.
- Simulations show clustering coefficient impacts final epidemic size, peak infections, and timing.
- Model predictions align well with Monte Carlo stochastic simulations.
Conclusions:
- The proposed model accurately describes infectious disease spread influenced by network structure.
- Increasing clustering coefficient acts as a barrier to disease propagation in SIR models.
- Network topology, specifically clustering, is a significant factor in epidemic dynamics.
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