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Updated: Jan 18, 2026

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Disease-driven network adaptivity: implications for epidemic dynamics.

V R Saiprasad1, D V Senthilkumar2, V K Chandrasekar1

  • 1Department of Physics, Centre for Nonlinear Science and Engineering, School of Electrical and Electronics Engineering, Shanmugha Arts Science Technology and Research Academy, Thanjavur, Tamil Nadu, India.

Journal of the Royal Society, Interface
|January 15, 2026
PubMed
Summary
This summary is machine-generated.

Adaptive networks dynamically adjust contacts during outbreaks, reducing disease spread. However, insufficient adaptivity or delayed responses can be less effective than static networks, highlighting the need for dynamic modeling in epidemic control.

Keywords:
COVID-19CoMix surveyadaptive networks

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Area of Science:

  • Epidemiology
  • Network Science
  • Mathematical Biology

Background:

  • Traditional epidemic models assume static contact networks.
  • Real-world disease outbreaks, like COVID-19, show dynamic changes in contact rates.
  • Evidence suggests individuals reduce contacts as infections rise.

Purpose of the Study:

  • To introduce a dynamic modeling approach for adaptive networks in disease spread.
  • To investigate how network adaptivity influences epidemic thresholds and outcomes.
  • To analyze the impact of varying adaptivity strengths on disease transmission.

Main Methods:

  • Developed a model where maximum links per individual adapt to the number of infected individuals.
  • Utilized a logistic function to model contact evolution, validated with CoMix survey data.
  • Employed effective-degree ODE formalism and stochastic network simulations for analysis.

Main Results:

  • Adaptive networks can significantly reduce epidemic size and alter critical thresholds.
  • Insufficient or delayed adaptivity may perform worse than static networks.
  • Higher adaptivity enhances resilience for highly transmissible diseases; lower adaptivity suits lower transmission rates.

Conclusions:

  • Dynamic network modeling is crucial for accurate epidemic prediction and intervention.
  • Network adaptivity is a key factor influencing epidemic dynamics.
  • Tailoring network adaptivity to disease characteristics is essential for effective control.