This study aims to structure and evaluate a newCOVID-19modelwhich predicts vaccination effect in theKingdom of Saudi Arabia(KSA)under Atangana-Baleanu-Caputo(ABC)fractional derivatives.On the statistical aspect,we ana...This study aims to structure and evaluate a newCOVID-19modelwhich predicts vaccination effect in theKingdom of Saudi Arabia(KSA)under Atangana-Baleanu-Caputo(ABC)fractional derivatives.On the statistical aspect,we analyze the collected statistical data of fully vaccinated people from June 01,2021,to February 15,2022.Then we apply the Eviews program to find the best model for predicting the vaccination against this pandemic,based on daily series data from February 16,2022,to April 15,2022.The results of data analysis show that the appropriate model is autoregressive integratedmoving average ARIMA(1,1,2),and hence,a forecast about the evolution of the COVID-19 vaccination in 60 days is presented.The theoretical aspect provides equilibrium points,reproduction number R0,and biologically feasible region of the proposed model.Also,we obtain the existence and uniqueness results by using the Picard-Lindel method and the iterative scheme with the Laplace transform.On the numerical aspect,we apply the generalized scheme of the Adams-Bashforth technique in order to simulate the fractional model.Moreover,numerical simulations are performed dependent on real data of COVID-19 in KSA to show the plots of the effects of the fractional-order operator with the anticipation that the suggested model approximation will be better than that of the established traditional model.Finally,the concerned numerical simulations are compared with the exact real available date given in the statistical aspect.展开更多
The present investigations are associated with designing Morlet wavelet neural network(MWNN)for solving a class of susceptible,infected,treatment and recovered(SITR)fractal systems of COVID-19 propagation and control....The present investigations are associated with designing Morlet wavelet neural network(MWNN)for solving a class of susceptible,infected,treatment and recovered(SITR)fractal systems of COVID-19 propagation and control.The structure of an error function is accessible using the SITR differential form and its initial conditions.The optimization is performed using the MWNN together with the global as well as local search heuristics of genetic algorithm(GA)and active-set algorithm(ASA),i.e.,MWNN-GA-ASA.The detail of each class of the SITR nonlinear COVID-19 system is also discussed.The obtained outcomes of the SITR system are compared with the Runge-Kutta results to check the perfection of the designed method.The statistical analysis is performed using different measures for 30 independent runs as well as 15 variables to authenticate the consistency of the proposed method.The plots of the absolute error,convergence analysis,histogram,performancemeasures,and boxplots are also provided to find the exactness,dependability and stability of the MWNN-GA-ASA.展开更多
基金supported by the Deanship of Scientific Research,Imam Mohammad Ibn Saud Islamic University (IMSIU),Saudi Arabia,Grant No. (21-13-18-069).
文摘This study aims to structure and evaluate a newCOVID-19modelwhich predicts vaccination effect in theKingdom of Saudi Arabia(KSA)under Atangana-Baleanu-Caputo(ABC)fractional derivatives.On the statistical aspect,we analyze the collected statistical data of fully vaccinated people from June 01,2021,to February 15,2022.Then we apply the Eviews program to find the best model for predicting the vaccination against this pandemic,based on daily series data from February 16,2022,to April 15,2022.The results of data analysis show that the appropriate model is autoregressive integratedmoving average ARIMA(1,1,2),and hence,a forecast about the evolution of the COVID-19 vaccination in 60 days is presented.The theoretical aspect provides equilibrium points,reproduction number R0,and biologically feasible region of the proposed model.Also,we obtain the existence and uniqueness results by using the Picard-Lindel method and the iterative scheme with the Laplace transform.On the numerical aspect,we apply the generalized scheme of the Adams-Bashforth technique in order to simulate the fractional model.Moreover,numerical simulations are performed dependent on real data of COVID-19 in KSA to show the plots of the effects of the fractional-order operator with the anticipation that the suggested model approximation will be better than that of the established traditional model.Finally,the concerned numerical simulations are compared with the exact real available date given in the statistical aspect.
基金The authors extend their appreciation to the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University for funding this work through Research Group No.RG-21-09-12.
文摘The present investigations are associated with designing Morlet wavelet neural network(MWNN)for solving a class of susceptible,infected,treatment and recovered(SITR)fractal systems of COVID-19 propagation and control.The structure of an error function is accessible using the SITR differential form and its initial conditions.The optimization is performed using the MWNN together with the global as well as local search heuristics of genetic algorithm(GA)and active-set algorithm(ASA),i.e.,MWNN-GA-ASA.The detail of each class of the SITR nonlinear COVID-19 system is also discussed.The obtained outcomes of the SITR system are compared with the Runge-Kutta results to check the perfection of the designed method.The statistical analysis is performed using different measures for 30 independent runs as well as 15 variables to authenticate the consistency of the proposed method.The plots of the absolute error,convergence analysis,histogram,performancemeasures,and boxplots are also provided to find the exactness,dependability and stability of the MWNN-GA-ASA.
