Mathematical Modelling to Predict the Effect of Vaccination on Delay and Rise of COVID-19 Cases Management

Abstract

In this paper, a mathematical model based on COVID-19 is developed to study and manage disease outbreaks. The effect of vaccination with regard to its efficacy and percentage of population vaccinated in a closed population is investigated. To study virus transmission, the system employs six nonlinear ordinary differential equations with susceptible–exposed–asymptomatic–infected–vaccinated–recovered populations and the basic reproduction number are calculated. The proposed model describes for highly infectious diseases (such as COVID-19) in a closed containment area with no migration. This paper considers that the percentage of vaccinated population has a significant impact on the number of COVID-19 positive cases during the pandemic wave and examines how the pandemic rise time is delayed. Numerical simulation to investigate disease outbreaks when the community is undergoing vaccination is performed, taking the efficacy rate of the vaccine into account. Sensitivity Index values are calculated for the reproduction number and their relations with few other parameters are depicted.

Recent Trends in EEG-Based Motor Imagery Signal Analysis and Recognition: A Comprehensive Review

“…True Positive (TP) refers to accurate identification of a condition or trait while False Positive (FP) refers to incorrect identification of a condition or trait. True Negative (TN) refers to accurate identification of the absence of a condition or trait whereas False Negative (FN) denotes incorrect identification of the absence of a condition or trait [81], [82], [83], Various performance measure can be defined Using TP, TN, FP and FN. Few of them are defined as:…”