Network-driven vaccination strategies for preventing rebound dynamics in metapopulation epidemic control

Area of Science:

• Epidemiology
• Mathematical Biology
• Network Science

Background:

• Epidemic control strategies, including vaccination, aim to halt disease transmission.
• Control measures can paradoxically lead to disease resurgence due to susceptible populations, a phenomenon termed the 'rebound effect' or 'honeymoon period'.
• Understanding the conditions triggering rebound dynamics is crucial for effective and sustainable epidemic management.

Purpose of the Study:

• To investigate the fundamental conditions driving rebound dynamics in epidemic control.
• To develop a mathematical criterion for identifying vaccination strategies prone to causing rebound.
• To propose and validate an alternative vaccination protocol that prevents rebound.

Main Methods:

• Utilized a metapopulation network framework to model epidemic spread.
• Derived a mathematical criterion based on metapopulation network structure to predict rebound.
• Analyzed and validated an alternative vaccination protocol designed to mitigate rebound dynamics.

Main Results:

• Identified key network structures and vaccination strategies that precipitate epidemic rebound.
• Developed a predictive criterion for rebound risk based solely on network topology.
• Demonstrated the efficacy of a proposed alternative vaccination protocol in preventing rebound dynamics.

Conclusions:

• The structure of metapopulation networks significantly influences the potential for epidemic rebound.
• Specific vaccination strategies can inadvertently promote resurgence, necessitating careful planning.
• A novel vaccination protocol offers a viable method for achieving sustainable epidemic control by eliminating rebound effects.