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Percolation with long-range correlations for epidemic spreading.

Z J Tan1, X W Zou, Z Z Jin

  • 1Department of Physics, Wuhan University, Wuhan 430072, People's Republic of China.

Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics
|January 4, 2001
PubMed
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This study introduces a percolation model to simulate epidemic spreading, revealing that long-range correlations significantly influence disease transmission thresholds and patterns. Stronger correlations alter epidemic spread dynamics, impacting critical behaviors.

Area of Science:

  • Complex systems
  • Epidemiology
  • Statistical physics

Background:

  • Epidemic spreading models are crucial for understanding disease transmission dynamics.
  • Percolation theory provides a framework for studying connectivity and phase transitions in various systems.
  • Long-range correlations can significantly alter system behavior compared to short-range interactions.

Purpose of the Study:

  • To investigate the impact of long-range correlations on epidemic spreading phenomena.
  • To analyze how varying correlation exponents and pathogenic ratios affect epidemic patterns.
  • To examine the critical behavior and threshold dynamics of epidemic percolation.

Main Methods:

  • Utilizing a percolation model incorporating long-range correlations.

Related Experiment Videos

  • Employing Monte Carlo simulations to explore different correlation strengths and pathogenic ratios.
  • Analyzing pattern formation (site percolation, Eden cluster, Leath cluster) and fractal dimensions.

    • Main Results:

    • Observed pattern transitions from site percolation to Eden or Leath clusters as correlation strengthens.
    • Demonstrated an increase in fractal dimension with pattern changes, approaching the space dimension.
    • Established that correlation strength critically influences the epidemic spreading threshold.

    Conclusions:

    • Long-range correlations are a key factor in determining epidemic spreading thresholds.
    • The model successfully captures the complex interplay between correlation, pathogenicity, and epidemic patterns.
    • Findings offer insights into the critical behavior of variant epidemics under varying conditions.