Compartmental and cellular automaton $SEIRS$ epidemiology models for the COVID-19 pandemic with the effects of temporal immunity and vaccination

TL;DR

This paper develops and analyzes $SEIRS$ models incorporating COVID-19 features like temporary immunity and vaccination, using both compartmental and cellular automaton approaches to evaluate disease dynamics and control measures.

Contribution

It introduces a combined compartmental and cellular automaton framework for $SEIRS$ models, including effects of temporary immunity and vaccination, with stability analysis and simulation of quarantine strategies.

Findings

Identified conditions for disease-free and endemic states.

Derived the basic reproductive number expression.

Simulated quarantine effects via cellular automaton.

Abstract

We consider the $SEIRS$ epidemiology model with such features of the COVID-19 outbreak as: abundance of unidentified infected individuals, limited time of immunity and a possibility of vaccination. Within a compartmental realization of this model, we found the disease-free and the endemic stationary states. They exist in their respective restricted regions of the le via linear stability analysis. The expression for the basic reproductive number is obtained as well. The positions and heights of a first peak for the fractions of infected individuals are obtained numerically and are fitted to simple algebraic forms, that depend on model rates. Computer simulations of a lattice-based realization for this model was performed by means of the cellular automaton algorithm. These allowed to study the effect of the quarantine measures explicitly, via changing the neighbourhood size. The attempt is made to match both quarantine and vaccination measures aimed on balanced solution for effective suppression of the pandemic.