Opinion-driven vaccination and epidemic dynamics on heterogeneous networks

Area of Science:

• Epidemiology
• Sociology
• Network Science

Background:

• Vaccination campaigns are crucial for infectious disease control.
• Public opinion and individual willingness to vaccinate significantly impact campaign success.
• Heterogeneous social networks and individual opinions influence disease spread.

Purpose of the Study:

• To investigate a coupled opinion-epidemic model on heterogeneous networks.
• To analyze how individual opinions, peer interactions, and risk perception affect vaccination behavior and epidemic dynamics.
• To examine the role of network structure in disease control strategies.

Main Methods:

• Utilized Monte Carlo simulations and a semi-analytical microscopic Markov-chain approach.
• Modeled opinion dynamics influenced by peer interaction and local risk perception.
• Employed scale-free networks, specifically Barabási-Albert structures, to represent network heterogeneity.

Main Results:

• Derived and validated analytical expressions for critical infection thresholds and stable vaccinated populations.
• Demonstrated that stronger local risk perception promotes pro-vaccination opinions and reduces infection rates.
• Observed that dominant peer influence can lead to increased long-term infection levels.

Conclusions:

• Effective vaccination strategies must consider social behavior and network structures.
• Risk perception plays a vital role in enhancing vaccination uptake and controlling epidemics.
• Network heterogeneity significantly influences the effectiveness of epidemic control measures.