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Contact Adaption During Epidemics: A Multilayer Network Formulation Approach.

Faryad Darabi Sahneh1, Aram Vajdi1, Joshua Melander1

  • 1Department of Electrical and Computer EngineeringKansas State UniversityManhattanKS66506.

IEEE Transactions on Network Science and Engineering
|June 30, 2021
PubMed
Summary
This summary is machine-generated.

Individuals adapt social contacts to prevent disease spread. This study models how adaptive contact networks impact epidemic dynamics, revealing that adaptation speed critically affects disease prevalence and network robustness against outbreaks.

Keywords:
Epidemicscontact adaptationmultilayer networksnonlinear Perron-Frobeniusstate-dependent switching networks

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Area of Science:

  • Epidemiology
  • Network Science
  • Mathematical Modeling

Background:

  • Human behavior, specifically altering social contacts, is a key factor in infectious disease transmission.
  • Existing mathematical models often overlook the interplay between disease dynamics and adaptive contact behaviors.

Purpose of the Study:

  • To develop and analyze a mathematical model of infectious disease propagation in a network with adaptive contact behaviors.
  • To investigate how contact adaptation influences epidemic thresholds and disease prevalence.

Main Methods:

  • A two-layer network model representing adaptive, state-dependent contact networks.
  • Analysis involving nonlinear Perron-Frobenius (NPF) problems to determine epidemic thresholds.
  • Mathematical modeling of agent-based contact switching upon detecting local infections.

Main Results:

  • Contact adaptation affects disease prevalence and epidemic thresholds nonlinearly.
  • The epidemic threshold is determined by a nonlinear Perron-Frobenius problem.
  • Slow contact adaptation can paradoxically decrease network robustness against epidemic spreading.

Conclusions:

  • Adaptive contact networks present complex dynamics in epidemic spreading.
  • The rate of contact adaptation is crucial for network robustness, with insufficient speed potentially increasing vulnerability.
  • This work provides analytical insights into disease dynamics within adaptive social structures.