The negative information-epidemic coupling model based on individual emotional responses in multiplex network

Area of Science:

• Epidemiology
• Network Science
• Computational Social Science

Background:

• Emotional responses significantly impact public health crises.
• Information diffusion and disease transmission are interconnected.
• Multiplex networks offer a framework to study complex interactions.

Purpose of the Study:

• To model the interplay between negative information diffusion and epidemic transmission.
• To analyze the influence of individual emotional responses (anxious vs. fair) in a multiplex network.
• To investigate how emotional states affect epidemic dynamics and information spread.

Main Methods:

• Developed a coupled model integrating emotional responses into information and epidemic layers.
• Utilized a threshold function for information diffusion influenced by emotional state.
• Employed a linear function for infected individuals' emotional impact on recovery rates.
• Applied Markov chain analysis for epidemic outbreak threshold and final size estimation.
• Conducted extensive Monte Carlo simulations to validate findings.

Main Results:

• Anxious individuals accelerate negative information diffusion and epidemic transmission.
• Higher proportions of anxious individuals lower outbreak thresholds and increase final epidemic size.
• Information diffusion is influenced by the emotional responses of an individual's neighborhood.
• Infected individuals' anxiety levels correlate with sensitivity to recovery rate interventions.
• Rational coping and reduced anxiety in infected individuals benefit epidemic control.

Conclusions:

• Individual emotional responses, particularly anxiety, play a critical role in epidemic dynamics and information spread.
• Targeted interventions to manage anxiety can mitigate epidemic consequences.
• Understanding network effects of emotional responses is crucial for effective public health strategies.