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Epidemic processes over adaptive state-dependent networks.

Masaki Ogura1, Victor M Preciado1

  • 1University of Pennsylvania, 3330 Walnut Street, Philadelphia, Pennsylvania 19104, USA.

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Summary
This summary is machine-generated.

This study introduces an adaptive network model to prevent epidemic spread by allowing individuals to cut connections to infected nodes. It provides a method to optimize adaptation rates for eradicating outbreaks in any network.

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Area of Science:

  • Epidemiology
  • Network Science
  • Computational Biology

Background:

  • Epidemic spread in networks is a critical concern.
  • Adaptive networks offer novel strategies for disease control.
  • Existing models often lack adaptability to network dynamics.

Purpose of the Study:

  • To analyze epidemic dynamics in adaptive networks.
  • To derive a lower bound for the epidemic threshold in the adaptive susceptible-infected-susceptible (ASIS) model.
  • To propose an algorithm for optimizing adaptation rates to control epidemics.

Main Methods:

  • Mathematical modeling of epidemic processes.
  • Derivation of a closed-form expression for the epidemic threshold lower bound.
  • Development and testing of an optimization algorithm for adaptation rates.
  • Numerical simulations to validate theoretical findings.

Main Results:

  • A closed-form lower bound for the epidemic threshold in ASIS models on arbitrary networks was derived.
  • For homogeneous dynamics, the bound relates to the static SIS model threshold, influenced by adaptation rates.
  • An efficient algorithm for optimal adaptation rate tuning was proposed.

Conclusions:

  • The derived lower bounds are tight and validated by simulations.
  • The proposed algorithm effectively aids in eradicating epidemic outbreaks.
  • Optimal adaptation rates can be determined and compared to network centrality measures.