Network-Driven Vaccination Strategies for Preventing Rebound Dynamics in Metapopulation Epidemic Control

TL;DR

This paper investigates how vaccination strategies in interconnected populations can prevent epidemic rebounds, providing a mathematical criterion for rebound risk and proposing a protocol to eliminate such dynamics.

Contribution

It introduces a mathematical framework to identify vaccination strategies that avoid rebound effects in metapopulation networks and proposes a new protocol to ensure sustainable epidemic control.

Findings

Derived a criterion to predict rebound risk based on network structure

Proposed a vaccination protocol that eliminates rebound dynamics

Validated the approach analytically with theoretical models

Abstract

A critical question in epidemic control concerns the minimal requirements for a vaccination campaign to effectively halt a contagion process. However, control measures can inadvertently trigger resurgence dynamics, driven by a reservoir of susceptible individuals left unexposed in the controlled wave. This phenomenon, known as the "rebound effect", is often preceded by a temporary drop in cases, termed usually as the "honeymoon period". In this study, we examine the fundamental conditions for rebound dynamics within a metapopulation network framework. By elucidating the mechanisms underlying rebound events, we derive a rigorous mathematical criterion that identifies, based solely on the metapopulation network structure, the specific vaccination strategies likely to precipitate a rebound. Additionally, we propose an alternative vaccination protocol designed to eliminate rebound dynamics entirely. This approach is analytically validated and offers a robust pathway toward sustainable epidemic control.