Nonlinear dynamics of COVID-19 SEIR infection model with optimal control analysis

Ankamah, Johnson De-Graft; Okyere, Eric; Appiah, Sampson Takyi; Nana-Kyere, Sacrifice

doi:10.28919/cmbn/5095

Cited by 2 publications

(3 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Already in the literature, there are some papers to understand the dynamics of novel coronavirus spread [ 13 , 16 – 18 , 31 , 34 , 35 , 37 , 38 , 40 , 41 ]. This coronavirus model proposes to fill the inadequacy of previous studies for analyzing the spread dynamics incorporating the effect on human consciousness of the novel COVID-19.…”

Section: Novel Coronavirus Model With Basic Propertiesmentioning

confidence: 99%

Mathematical Analysis of Two Waves of COVID-19 Disease with Impact of Vaccination as Optimal Control

Santra

Ghosh²,

Mahapatra³

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

This paper is devoted to answering some questions using a mathematical model by analyzing India’s first and second phases of the COVID-19 pandemic. A new mathematical model is introduced with a nonmonotonic incidence rate to incorporate the psychological effect of COVID-19 in society. The paper also discusses the local stability and global stability of an endemic equilibrium and a disease-free equilibrium. The basic reproduction number is evaluated using the proposed COVID-19 model for disease spread in India based on the actual data sets. The study of nonperiodic solutions at a positive equilibrium point is also analyzed. The model is rigorously studied using MATLAB to alert the decision-making bodies to hinder the emergence of any other pandemic outbreaks or the arrival of subsequent pandemic waves. This paper shows the excellent prediction of the first wave and very commanding for the second wave. The exciting results of the paper are as follows: (i) psychological effect on the human population has an impact on propagation; (ii) lockdown is a suitable technique mathematically to control the COVID spread; (iii) different variants produce different waves; (iv) the peak value always crosses its past value.

show abstract

Section: Novel Coronavirus Model With Basic Propertiesmentioning

confidence: 99%

Mathematical Analysis of Two Waves of COVID-19 Disease with Impact of Vaccination as Optimal Control

Santra

Ghosh²,

Mahapatra³

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…Regarding the optimization of COVID-19 prediction models, three main approaches have been reported in the literature. The first uses the SEIR (Susceptible -Exposed -Infectious -Recovered) model (or its derivatives) as its basis and applies machine learning and optimization methods to determine the epidemiological parameters of the model [6][7][8][9][10][11][12][13][95][96][97][98][99][100][101][102][103][104][105][106]. The second approach uses a population-based model to simulate the transmission of the virus [14,15].…”

Section: B Predictionmentioning

confidence: 99%

“…A key question is how to return to normalcy and revive the economy without sparking another wave of COVID-19 cases and overwhelming hospitals. Optimal control problem formulations have been a popular way of modeling and optimizing mitigation strategies and assessing their effect on the spread of the virus [98,[151][152][153][154][155][156][157][158][159]. These frameworks are based on the SEIR model and utilize reinforcement learning (RL) [152,154] and optimization approaches [153,[160][161][162] to determine the optimal exit strategies.…”

Section: Prevention and Control: Curbing The Spread And Mitigating The Effectsmentioning

confidence: 99%

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

et al. 2021

View full text Add to dashboard Cite

This paper presents an overview of some key results from a body of optimization studies that are specifically related to COVID-19, as reported in the literature during 2020-2021. As shown in this paper, optimization studies in the context of COVID-19 have been used for many aspects of the pandemic. From these studies, it is observed that since COVID-19 is a multifaceted problem, it cannot be studied from a single perspective or framework, and neither can the related optimization models. Four new and different frameworks are proposed that capture the essence of analyzing COVID-19 (or any pandemic for that matter) and the relevant optimization models. These are: (i) microscale vs. macroscale perspective; (ii) early stages vs. later stages perspective; (iii) aspects with direct vs. indirect relationship to COVID-19; and (iv) compartmentalized perspective. To limit the scope of the review, only optimization studies related to the prediction and control of COVID-19 are considered (public health focused), and which utilize formal optimization techniques or machine learning approaches. In this context and to the best of our knowledge, this survey paper is the first in the literature with a focus on the prediction and control related optimization studies. These studies include optimization of screening testing strategies, prediction, prevention and control, resource management, vaccination prioritization, and decision support tools. Upon reviewing the literature, this paper identifies current gaps and major challenges that hinder the closure of these gaps and provides some insights into future research directions.

show abstract

Nonlinear dynamics of COVID-19 SEIR infection model with optimal control analysis

Cited by 2 publications

References 47 publications

Mathematical Analysis of Two Waves of COVID-19 Disease with Impact of Vaccination as Optimal Control

Mathematical Analysis of Two Waves of COVID-19 Disease with Impact of Vaccination as Optimal Control

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

Contact Info

Product

Resources

About