Modelling SARS-CoV-2 epidemics via compartmental and cellular automaton SEIRS model with temporal immunity and vaccination

TL;DR

This paper develops a comprehensive SEIRS model incorporating COVID-19 features like temporary immunity and vaccination, analyzing disease dynamics and control strategies through both compartmental and cellular automaton approaches.

Contribution

It introduces a hybrid modeling framework combining compartmental and cellular automaton models to study COVID-19 epidemic dynamics and control measures.

Findings

Identified stable disease-free and endemic states in the model.

Analyzed the basic reproductive number considering quarantine and vaccination.

Studied social distancing effects via cellular automaton simulations.

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. The control of the pandemic dynamics is possible by restricting the transmission rate, increasing identification and isolation rate of infected individuals, and via vaccination. For the compartmental version of this model, we found stable disease-free and endemic stationary states. The basic reproductive number is analysed with respect to balancing quarantine and vaccination measures. The positions and heights of the first peak of outbreak are obtained numerically and fitted to simple in usage algebraic forms. Lattice-based realization of this model is studied by means of the asynchronous cellular automaton algorithm. This permitted to study the effect of social distancing by varying the neighbourhood size of the model. The attempt is made to match the quarantine and vaccination effects.