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

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 susceptible–exposed–infected–recovered–dead model by introducing a vaccination term. One of our main assumptions is that the infection rate ([Formula: see text]) 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 ([Formula: see text]) 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 ([Formula: see text]) and the vaccination rate ([Formula: see text]) 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 ([Formula: see text]) and the vaccination rate ([Formula: see text]) on the infectious cases of COVID-19 and made some robust conclusions regarding the global behavior of this pandemic in near future.

Investigating the Impact of Segregation and Integration on Infection Dynamics: Analysis of a Cholera Model in a Two-Population System

“…The transmission dynamics of infectious diseases have undergone comprehensive studies through the application of the Susceptible-Infectious-Recovered (SIR) compartmental model, initially introduced by Kermack and McKendrick [17], and its subsequent refinements and adaptations. Over the years, this model has been employed to analyze a spectrum of diseases, ranging from historical pandemics such as the Spanish flu to endemic illnesses like Cholera, Malaria, and Pneumonia, as well as contemporary challenges like the seasonal flu and the ongoing COVID-19 pandemic [18, 19, 20, 21, 22, 23, 24, 25, 26]. The versatility of these compartmental models has been demonstrated in their ability to effectively simulate and predict the trajectories of disease spread, accounting for variables such as mitigation measures, sanitation practices, social distancing initiatives, and vaccination campaigns.…”