An evolutionary vaccination game in the modified activity driven network by considering the closeness

Area of Science:

• Epidemiology
• Network Science
• Game Theory

Background:

• Epidemic modeling often simplifies social interactions.
• Understanding vaccination dynamics in complex networks is crucial.
• Activity-driven networks offer a realistic framework for social behavior.

Purpose of the Study:

• To investigate an evolutionary vaccination game within a modified activity-driven network.
• To analyze the impact of a 'closeness' parameter on epidemic spread and vaccination.
• To explore the relationship between individual activity levels and disease/vaccination states.

Main Methods:

• Simulations of an evolutionary vaccination game.
• Utilizing a modified activity-driven network model.
• Introducing and analyzing a 'closeness' parameter ($$) to define connections.
• Defining variables to link individual activity with disease states.

Main Results:

• The closeness parameter ($$) can mitigate both epidemic spread and vaccination uptake.
• Without vaccination, recovered density rises with the infection-to-recovery rate ratio ($$).
• With vaccination, recovered density initially increases then decreases with $$.
• Higher individual activity correlates with increased recovered and vaccinated frequencies.
• Closeness has a greater influence on vaccination decisions than immune fees.
• Within a specific range, increasing $$ reduces overall recovered frequency.

Conclusions:

• Social network structure, specifically 'closeness,' significantly influences epidemic and vaccination dynamics.
• Individual activity levels play a key role in disease transmission and prevention.
• Vaccination strategies can have unintended consequences, potentially backfiring under certain conditions.
• The interplay between network topology, individual behavior, and disease parameters is complex and requires careful consideration.