Epidemic Extinction in a Continuous SIRS Model with Vaccination

TL;DR

This paper investigates the conditions for epidemic extinction in a continuous SIRS model with vaccination, emphasizing the limitations of deterministic models and the need for stochastic considerations.

Contribution

It provides a detailed analysis of how different parameters influence epidemic persistence and highlights the shortcomings of continuous models in predicting extinction.

Findings

Continuous models can predict unrealistically long epidemic persistence.

Parameter regimes significantly affect the likelihood of epidemic extinction.

Stochastic effects are crucial for accurate epidemic fade-out modeling.

Abstract

Epidemics have shaped human history, often with devastating consequences, motivating the development of mathematical models to understand and control their dynamics. Among the many aspects of epidemic behavior, the conditions that lead to epidemic extinction stand out as a central-if not the fundamental-question in epidemic modeling. In this work, we study epidemic extinction in a continuous SIRS (Susceptible-Infected-Recovered-Susceptible) model governed by a system of ordinary differential equations (ODEs). The model includes vaccination as a time-dependent process and considers the reinfection of recovered individuals through waning immunity. We analyze how different parameter regimes -- particularly infection, recovery, and immunity loss rates -- affect the persistence or extinction of the epidemic. Special attention is given to the limitations of continuous population models, in which the infected fraction can fall below the equivalent of a single individual, leading to nonphysical outcomes such as unrealistically long persistence or artificial secondary peaks. By comparing the continuous SIRS dynamics with expected real-world thresholds for extinction, we highlight the importance of incorporating stochasticity or discrete effects to accurately describe epidemic fade-out.