Coupling dynamics of epidemic spreading and information diffusion on complex networks.

Area of Science:

• Complex systems science
• Epidemiology
• Information diffusion dynamics

Background:

• Disease spread and information transmission are intertwined processes within populations.
• Understanding this interplay is crucial for public health and risk management.
• Previous research often studied these dynamics in isolation.

Purpose of the Study:

• To analyze the correlation between disease propagation (H7N9, Dengue fever) and online information spread.
• To develop a nonlinear model interpreting the coupling effect on epidemic dynamics.
• To investigate the emergence of multi-outbreak phenomena due to coupled dynamics.

Main Methods:

• Empirical analysis of real-world disease data (H7N9, Dengue fever) and internet information.
• Development of a nonlinear Susceptible-Infected-Susceptible (SIS) model.
• Theoretical analysis and computer simulations to validate model predictions.

Main Results:

• High correlation observed between disease spread and information diffusion patterns.
• High epidemic prevalence leads to slower information decay, paradoxically increasing infection levels.
• Coupled dynamics can lead to multi-outbreak phenomena, consistent with empirical observations.

Conclusions:

• The interplay between epidemic spreading and information diffusion significantly influences disease dynamics.
• A feedback loop exists where high infection rates affect information decay and vice versa.
• This research provides insights into complex epidemic behaviors and informs public health strategies.