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Epidemic variability in complex networks.

Pascal Crépey1, Fabián P Alvarez, Marc Barthélemy

  • 1UMR-S 707, INSERM, Paris, F-75012, France.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|May 23, 2006
PubMed
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Disease outbreaks on complex networks show high sensitivity to noise during early stages. Hubs are unreliable for early detection due to fluctuating infection times, impacting containment strategies.

Area of Science:

  • Epidemiology
  • Network Science
  • Computational Biology

Background:

  • Understanding disease dynamics on complex networks is crucial for public health.
  • Previous models often simplify network structures and temporal dynamics.

Purpose of the Study:

  • To numerically investigate disease outbreak variability on homogeneous and scale-free networks.
  • To analyze the impact of initial conditions, node degree, and network topology on epidemic spread and timing.

Main Methods:

  • Susceptible-Infected (SI) model simulation.
  • Analysis of short-term epidemic dynamics.
  • Numerical study of infection time dependence on node properties and network structure.

Main Results:

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  • Identified a time regime with high prevalence sensitivity to noise.
  • Demonstrated that infection time depends on node degree and distance to the initial seed.
  • Showed that hubs exhibit significant infection time fluctuations, limiting their use for early detection.
  • Found that multiple paths, even long ones, reduce average infection time.

Conclusions:

  • Network structure and initial conditions significantly influence epidemic variability.
  • Hub reliability for early detection is limited by stochasticity in infection spread.
  • Findings inform the development of adaptive, time-dependent disease containment strategies.