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A network epidemic model for online community commissioning data.

Clement Lee1,2, Andrew Garbett2, Darren J Wilkinson1

  • 11School of Mathematics and Statistics, Newcastle University, Newcastle upon Tyne, UK.

Statistics and Computing
|January 28, 2020
PubMed
Summary

This study introduces a network epidemic model combining stochastic models and preferential attachment networks to better understand disease spread. Simulations revealed parameter identifiability issues, highlighting challenges in real-world network analysis.

Keywords:
Community commissioningMCMCPreferential attachmentRandom graphsStochastic epidemic models

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Area of Science:

  • Network Science
  • Epidemiology
  • Computational Statistics

Background:

  • Real-life networks are often better described by preferential attachment models than Bernoulli random graphs.
  • Understanding disease propagation in social networks is crucial.

Purpose of the Study:

  • To propose a novel network epidemic model integrating stochastic epidemic and preferential attachment models.
  • To analyze the spread of "infection" within social networks.

Main Methods:

  • Developed a network epidemic model by combining stochastic epidemic and preferential attachment models.
  • Employed Markov Chain Monte Carlo (MCMC) simulations to study model identifiability.
  • Applied the model to online commissioning data.

Main Results:

  • The proposed model, based on preferential attachment networks, offers a more realistic representation of social networks for epidemic studies.
  • Simulation results using MCMC indicated an identifiability issue with the model parameters.
  • The model was successfully applied to analyze online commissioning data.

Conclusions:

  • The integration of preferential attachment networks into epidemic models enhances realism for social network analysis.
  • Identifiability issues in model parameters present challenges for accurate real-world application.
  • The study provides a framework for analyzing epidemic dynamics on complex networks.