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Epidemics in adaptive social networks with temporary link deactivation
Ilker Tunc1, Maxim S Shkarayev2, Leah B Shaw1
1Department of Applied Science, College of William and Mary, Williamsburg, VA 23187.
Journal of Statistical Physics
|December 24, 2013
Summary
Individuals can adapt social networks to slow disease spread by deactivating contacts with sick people. This network adaptation significantly impacts epidemic dynamics, especially in fast-response scenarios.
Area of Science:
- Epidemiology
- Network Science
- Mathematical Biology
Background:
- Disease transmission is influenced by social contact network topology.
- Individual behavioral changes can alter network structure, affecting disease spread dynamics.
Purpose of the Study:
- To investigate how individuals adapting their social contacts affects disease spread.
- To model and analyze disease dynamics on an adaptive network.
Main Methods:
- Developed a mean-field description of the adaptive network model.
- Utilized numerical simulations to study disease spread on the adaptive network.
- Analyzed the bifurcation structure of the system.
Main Results:
- Identified two distinct regimes: slow and fast network dynamics.
- Demonstrated that efficient adaptation in fast dynamics significantly reduces disease spread.
- Observed a single parameter-dependent stable steady state.
- Found a non-monotonic relationship between connectivity and link deactivation rate.
Conclusions:
- Adaptive social networks can be a crucial factor in controlling epidemic spread.
- The speed of network adaptation critically determines its effectiveness in mitigating disease.
- The study provides insights into the complex interplay between individual behavior, network structure, and disease dynamics.
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