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Modified diffusive epidemic process on Apollonian networks.

David Alencar1, Antonio Filho2, Tayroni Alves1

  • 1Departamento de Física, Universidade Federal do Piauí, 57072-970, Teresina, PI, Brazil.

Journal of Biological Physics
|April 28, 2023
PubMed
Summary
This summary is machine-generated.

This study models epidemic spreading in non-sedentary populations using Apollonian networks and computational analysis. The findings reveal a continuous phase transition with unique critical exponents, differing from standard models.

Keywords:
DEP modelEpidemic spreadingPhase transition

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Area of Science:

  • Epidemiology
  • Network Science
  • Computational Physics

Background:

  • Epidemic spreading models often simplify population dynamics.
  • Non-sedentary populations present unique challenges for disease transmission modeling.
  • Apollonian networks offer a realistic framework for complex, non-sedentary social structures.

Purpose of the Study:

  • To analyze epidemic spreading on Apollonian networks.
  • To model disease transmission in non-sedentary populations.
  • To investigate the phase transition dynamics of epidemic models.

Main Methods:

  • Utilized the Monte Carlo method for computational analysis.
  • Developed a modified diffusive epidemic model with two classes: susceptible (A) and infected (B).
  • Applied Gillespie's algorithm for reaction dynamics and finite-size scaling analysis.

Main Results:

  • The model demonstrates a continuous phase transition to an absorbing state.
  • Identified a set of critical exponents characterizing the transition across three diffusive regimes.
  • Observed that these critical exponents differ from those in the mean-field universality class.

Conclusions:

  • The proposed model provides a more realistic approach to epidemic spreading in dynamic populations.
  • The unique critical exponents suggest a distinct universality class for epidemic dynamics on Apollonian networks.
  • This research contributes to understanding complex system behaviors and disease control strategies.