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Following the Dynamics of Structural Variants in Experimentally Evolved Populations
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Published on: February 3, 2023

Epidemic spreading in evolving networks.

Yonathan Schwarzkopf1, Attila Rákos, David Mukamel

  • 1Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|January 15, 2011
PubMed
Summary
This summary is machine-generated.

Epidemic spreading is typically suppressed by network rewiring, contrary to expectations. This study analyzes epidemic dynamics on scale-free networks, revealing altered infection thresholds and reduced prevalence in rewiring networks.

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Area of Science:

  • Complex Systems
  • Epidemiology
  • Network Science

Background:

  • Understanding epidemic spreading is crucial for public health interventions.
  • Network structure significantly influences disease transmission dynamics.
  • Previous models often assumed static network structures.

Purpose of the Study:

  • To introduce and analyze a model for epidemic spreading on rewiring networks.
  • To investigate the impact of network rewiring on epidemic suppression.
  • To compare epidemic dynamics in rewiring versus static scale-free networks.

Main Methods:

  • Development of a mathematical model for epidemic spreading.
  • Analysis of the model on scale-free steady-state networks.
  • Mean-field approximation applied to assess infection thresholds and prevalence.

Main Results:

  • Network rewiring generally suppresses epidemic spreading.
  • For degree distribution exponent γ>3, rewiring networks show a higher infection threshold compared to static networks.
  • For 2<γ≤3, no threshold exists, but rewiring networks exhibit lower steady-state prevalence.

Conclusions:

  • The rewiring process in networks can act as a mechanism to control epidemic outbreaks.
  • Network dynamics, specifically rewiring, significantly alter epidemic thresholds and disease prevalence.
  • Findings challenge naive expectations and highlight the importance of considering dynamic network structures in epidemiological modeling.