A robust prediction from a minimal model of COVID-19 -- Can we avoid the third wave?

TL;DR

This paper models COVID-19's third wave using a modified SEIRD framework with oscillatory infection rates and vaccination, providing insights into controlling future outbreaks through parameter analysis.

Contribution

It introduces a minimalistic SEIRD model with oscillatory infection rates and vaccination, highlighting key parameters influencing the pandemic's future trajectory.

Findings

Oscillatory infection rate models seasonal effects.

Vaccination rate significantly impacts third wave prevention.

Interplay between infection and vaccination rates determines outbreak severity.

Abstract

COVID-19 pandemic is one of the major disasters that humanity has ever faced. In this paper, we try to model the effect of vaccination in controlling the pandemic, particularly in context to the third wave which is predicted to hit globally. Here we have modified the SEIRD model by introducing a vaccination term. One of our main assumptions is that the infection rate (\b{eta}(t)) is oscillatory. This oscillatory nature has been discussed earlier in literature with reference to the seasonality of epidemics. However, in our case we invoke this nature of the infection rate (\b{eta}(t)) to model the cyclical behavior of the COVID-19 pandemic within a short period. This study focuses on a minimalistic approach where we have logically deduced that the infection rate (\b{eta}(t)) and the vaccination rate ({\lambda}) are the most important parameters while the other parameters can be assumed to be constants throughout the simulation. Finally, we have studied the rich interplay between the infection rate (\b{eta}(t)) and the vaccination rate ({\lambda}) on the infectious cases of COVID-19 and made some robust conclusions regarding the global behavior of this pandemic in near future.