Projections and fractional dynamics of COVID-19 with optimal control analysis

Nabi, Khondoker Nazmoon; Kumar, Pushpendra; Ertürk, Vedat Suat

doi:10.1101/2020.11.17.20233031

Cited by 5 publications

(5 citation statements)

References 33 publications

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“…In [36] , the authors studied the dynamics of COVID-19 in Cameroon by a realistic data with the proposal of a new non-linear model. Also in [37] , researchers derived the solution of the model with optimal control analysis in Atangana-Baleanu fractional derivative sense. So after getting a strong motivation from these works, the re-formulation of the proposed integer order model (3) in the Atangana-Baleanu fractional derivative sense is defined as follows:

Here,

is the Atangana-Baleanu non-integer order derivative operator of order

.…”

Section: Model Structure In Fractional Sensementioning

confidence: 99%

A new fractional mathematical modelling of COVID-19 with the availability of vaccine

2021

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The most dangerous disease of this decade novel coronavirus or COVID-19 is yet not over. The whole world is facing this threat and trying to stand together to defeat this pandemic. Many countries have defeated this virus by their strong control strategies and many are still trying to do so. To date, some countries have prepared a vaccine against this virus but not in an enough amount. In this research article, we proposed a new SEIRS dynamical model by including the vaccine rate. First we formulate the model with integer order and after that we generalise it in Atangana-Baleanu derivative sense. The high motivation to apply Atangana-Baleanu fractional derivative on our model is to explore the dynamics of the model more clearly. We provide the analysis of the existence of solution for the given fractional SEIRS model. We use the famous Predictor-Corrector algorithm to derive the solution of the model. Also, the analysis for the stability of the given algorithm is established. We simulate number of graphs to see the role of vaccine on the dynamics of the population. For practical simulations, we use the parameter values which are based on real data of Spain. The main motivation or aim of this research study is to justify the role of vaccine in this tough time of COVID-19. A clear role of vaccine at this crucial time can be realised by this study.

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Here,

is the Atangana-Baleanu non-integer order derivative operator of order

.…”

Section: Model Structure In Fractional Sensementioning

confidence: 99%

A new fractional mathematical modelling of COVID-19 with the availability of vaccine

2021

Self Cite

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“…We have considered the initial data

, and the parametric values

from the [14] . And for more detail about the numerical results and fractional order modeling, we refer the readers to the work in [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] .…”

Section: Numerical Schemementioning

confidence: 99%

A fractional order Covid-19 epidemic model with Mittag-Leffler kernel

Khan

Ibrahim

Abdel‐Aty

et al. 2021

Chaos, Solitons & Fractals

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In this article, we are studying fractional-order COVID-19 model for the analytical and computational aspects. The model consists of five compartments including; which denotes susceptible class, represents exposed population, is the class for infected people who have been developed with COVID-19 and can cause spread in the population. The recovered class is denoted by and is the concentration of COVID-19 virus in the area. The computational study shows us that the spread will be continued for long time and the recovery reduces the infection rate. The numerical scheme is based on the Lagrange’s interpolation polynomial and the numerical results for the suggested model are similar to the integer order which gives us the applicability of the numerical scheme and effectiveness of the fractional order derivative.

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“…In the face of the another wave of infection, pandemic fatigue and public apathy towards various intervention strategies have prompted people to act impetuously, imposing new challenges to the government officials. Different non-pharmaceutical intervention strategies such as wearing efficacious face coverings, closure of educational institutions, strict travel restrictions, and strict containment measures are the most powerful intervention strategies to flatten the epidemic curve [3] , [4] , [5] . In addition, mass level testing and tracing program are also indispensable for breaking the continuous transmission chain.…”

Section: Introductionmentioning

confidence: 99%

Forecasting COVID-19 cases: A comparative analysis between recurrent and convolutional neural networks

et al. 2021

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Though many countries have already launched COVID-19 mass vaccination programs to control the disease outbreak quickly, numerous countries around worldwide are grappling with unprecedented surges of new COVID-19 cases due to a more contagious and deadly variant of coronavirus. As the number of new cases is skyrocketing, pandemic fatigue and public apathy towards different intervention strategies pose new challenges to government officials to combat the pandemic. Henceforth, it is indispensable for the government officials to understand the future dynamics of COVID-19 flawlessly to develop strategic preparedness and resilient response planning. In light of the above circumstances, probable future outbreak scenarios in Brazil, Russia, and the United kingdom have been sketched in this study with the help of four deep learning models: long short term memory (LSTM), gated recurrent unit (GRU), convolutional neural network (CNN) and multivariate convolutional neural network (MCNN). In our analysis, the CNN algorithm has outperformed other deep learning models in terms of validation accuracy and forecasting consistency. It is unearthed in our study that CNN can provide robust long-term forecasting results in time-series analysis due to its capability of essential features learning, distortion invariance, and temporal dependence learning. However, the prediction accuracy of the LSTM algorithm has been found to be poor as it tries to discover seasonality and periodic intervals from any time-series dataset, which were absent in our studied countries. Our study has highlighted the promising validation of using convolutional neural networks instead of recurrent neural networks when forecasting with very few features and less amount of historical data.

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Projections and fractional dynamics of COVID-19 with optimal control analysis

Cited by 5 publications

References 33 publications

A new fractional mathematical modelling of COVID-19 with the availability of vaccine

A new fractional mathematical modelling of COVID-19 with the availability of vaccine

A fractional order Covid-19 epidemic model with Mittag-Leffler kernel

Forecasting COVID-19 cases: A comparative analysis between recurrent and convolutional neural networks

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