Infection spreading in a population with evolving contacts

Area of Science:

• Epidemiology
• Network Science
• Mathematical Biology

Background:

• The Susceptible-Infected-Susceptible (SIS) model is a fundamental framework for studying infectious disease dynamics.
• Network structures significantly influence disease transmission patterns.
• Agent behavior, such as link modification, can alter disease spread.

Purpose of the Study:

• To investigate the impact of dynamic network coevolution on infection spreading within an SIS model.
• To determine the effectiveness of agent-based link modification (breaking and reconnecting) in controlling epidemics.
• To assess the threshold for infection suppression based on reconnection frequency and isolation levels.

Main Methods:

• Simulation of an SIS epidemiological model on a coevolving network.
• Modeling susceptible agents breaking links with infected agents.
• Implementing two link-breaking outcomes: permanent removal and reconnection.
• Analyzing the effect of varying reconnection frequencies and isolation levels on infection prevalence.

Main Results:

• A moderate frequency of link reconnection among agents is sufficient to completely suppress infection spread.
• Even partial and weak isolation of infected agents can effectively eliminate the endemic state.
• The coevolution of the network structure with the infection dynamics plays a crucial role in disease control.

Conclusions:

• Dynamic network adaptation, specifically through link reconnection, offers a potent strategy for epidemic suppression.
• Targeted, non-stringent isolation measures can be highly effective in eradicating endemic infections in networked populations.
• The interplay between disease dynamics and network topology is critical for understanding and controlling infectious diseases.