Third wave of COVID-19: mathematical model with optimal control strategy for reducing the disease burden in Nigeria

Abstract

The study of COVID-19 pandemic which paralyzed global economy of countries is a crucial research area for effective future planning against other epidemics. Unfortunately, we now have variants of the disease resulting to what is now known as waves of the pandemic. Several mathematical models have been developed to study this disease. While recent models incorporated control measures, others are without optimal control measures or demographic parameters. In this study, we propose a deterministic compartmental epidemiological model to study the transmission dynamic of the spread of the third wave of the pandemic in Nigeria, and we incorporated optimal control measures as strategies to reduce the burden of the deadly disease. Specifically, we investigated the transmission dynamics of COVID-19 model without demographic features. We then conducted theoretical analysis of the model with and without optimal control strategy. In the model without optimal control, we computed the reproduction number, an epidemiological threshold useful for bringing the third wave of the pandemic under check in Nigeria, and we proofed the disease stability and conducted sensitivity analysis in order to identify parameters that can impact the reproduction number tremendously. In a similar reasoning, for the model with control strategy, we check the necessary condition for the model. To validate our theoretical analyses, we illustrated the applications of the proposed model using COVID-19 data for Nigeria for a period when the country was under the yoke of the third wave of the disease. The data were then fitted to the model, and we derived a predictive tool toward making a forecast for the cumulative number of cases of infection, cumulative number of active cases and the peak of the third wave of the pandemic. From the simulations, it was observed that the presence of optimal control parameters leads to significant impact on the reduction of the spread of the disease. However, it was discovered that the success of the control of the disease relies on the proper and effective implementation of the optimal control strategies efficiently and adequately.

Sensitivity Analysis and Optimal Control of Covid 19 Model

“…The results of the optimal control of COVID-19 transmission in two countries, the USA and India, were numerically calculated. In accordance with Omede et al [37], a control variable was assumed. In this study, we chose to determine the variable control 's parameters ν = 0, A 1 = 30%, A 1 = 70%, b 1 = 60%, b 2 = 20%, and b 3 = 20%, as shown in Figure 1.…”

An Estimate of Time-Dependent Transmission of COVID-19 Pandemic in Katsina State from June to November 2021

“…This information is represented in Figure 5. (Badmus et al, 2023;Omede et al, 2023;Onwube et al, 2023;Adeyemi et al, 2023;Moroh et al, 2023;Taiwo et al, 2023) and elsewhere around the world (Triambak et al, 2023;Jameel et al, 2023;Mitra et al, 2023) focusing on understanding the behaviour of the disease through modelling, reproduction metrics, risk factors and other research approaches.…”

The dynamics of hybrid-immune and immunodeficient susceptible individuals and the three stages of COVID-19 vaccination

“…A number of works have been done in modelling the dynamics of the spread of COVID-19 which are purely based on deterministic approach. Among such works are the works of Ngonghala et al [21], Adewole et al [22], Omede et al [23]; and a some modifications/improvements have been made to capture the dynamics of the spread of COVID-19. For example, in order to improve the accuracy for prediction of COVID-19 spread, Zhu et al [24] proposed a deterministic model by introducing the re-infection rate and social distancing factor into the traditional SEIRD to account for the effects of re-infection and social distancing on COVID-19 spread.…”