Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks
Tomokatsu Onaga1,2, James P Gleeson2, Naoki Masuda3
1Department of Physics, Kyoto University, Kyoto 606-8502, Japan.
Physical Review Letters
|September 27, 2017
Summary
Network dynamics significantly impact epidemic spread. Low concurrency can suppress epidemics, while high concurrency can enhance them, altering the epidemic threshold.
Area of Science:
- Epidemiology
- Network Science
- Mathematical Biology
Background:
- Epidemic processes in humans and animals occur on dynamic social contact networks.
- Infection spreading on temporal networks differs from static networks.
Purpose of the Study:
- To investigate the effect of concurrency on epidemic thresholds in temporal network models.
- To understand how network dynamics influence epidemic suppression or enhancement.
Main Methods:
- Theoretical investigation of stochastic susceptible-infected-susceptible (SIS) dynamics.
- Analysis of concurrency's impact on epidemic threshold in temporal networks.
- Analytical determination of concurrency-induced transition phases.
Main Results:
- Network dynamics can suppress epidemics (higher threshold) at low node concurrency.
- Network dynamics can enhance epidemics (lower threshold) at high node concurrency.
- Distinct phases of concurrency-induced transitions were identified.
Conclusions:
- Concurrency is a critical factor modulating epidemic thresholds in dynamic networks.
- Understanding these network dynamics is crucial for predicting and controlling infectious disease spread.
- The study provides a framework for analyzing epidemic behavior on temporal networks.
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