Analysis and numerical solution of novel fractional model for dengue

Ahmad, Shakoor; Javeed, Shumaila; Ahmad, Hijaz; Khushi, Jamila; Elagan, S. K.; Khames, Ahmed

doi:10.1016/j.rinp.2021.104669

Cited by 19 publications

(7 citation statements)

References 16 publications

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“…The designed neuro-evolution based computing strategy is a good alternative to exploit the state of the art on studies related to COVID-19 and dengue modeling and control [39][40][41][42][43].…”

Section: Discussionmentioning

confidence: 99%

Numerical Investigations through ANNs for Solving COVID-19 Model

Umar

Sabir

Raja

et al. 2021

IJERPH

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The current investigations of the COVID-19 spreading model are presented through the artificial neuron networks (ANNs) with training of the Levenberg-Marquardt backpropagation (LMB), i.e., ANNs-LMB. The ANNs-LMB scheme is used in different variations of the sample data for training, validation, and testing with 80%, 10%, and 10%, respectively. The approximate numerical solutions of the COVID-19 spreading model have been calculated using the ANNs-LMB and compared viably using the reference dataset based on the Runge-Kutta scheme. The obtained performance of the solution dynamics of the COVID-19 spreading model are presented based on the ANNs-LMB to minimize the values of fitness on mean square error (M.S.E), along with error histograms, regression, and correlation analysis.

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Section: Discussionmentioning

confidence: 99%

Numerical Investigations through ANNs for Solving COVID-19 Model

Umar

Sabir

Raja

et al. 2021

IJERPH

Self Cite

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“…During the past few years, the majority of the world's population has been affected by infectious diseases like HIV, hepatitis, influenza, and COVID-19. Dengue fever is one of the serious viral infectious diseases that can spread through a mosquito [1]. It has spread around 2.5 billion individuals worldwide, largely the hot seasonal countries, for instance, China, Pakistan, and so forth [2].…”

Section: Introductionmentioning

confidence: 99%

Swarm-optimized numerical investigation of Dengue Fever Model

Riaz,

Ullah,

Alasiry

et al. 2024

Preprint

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The current study aims to present a swarm-optimized technique for the numerical treatment of dengue fever non-linear model. The model is composed of the coupled nonlinear system comprising the susceptible, infected, and recovered compartments. The system is transformed into an unsupervised single layer feed-forward artificial neural network with a Mexican hat wavelet activation function in the hidden layer. The unknowns of the neural network is optimized with particle swarm optimization as an efficient global search aided by the effective local search technique based on sequential quadratic programming. The presented results are compared with state of art Runge-Kutta method and other modern reported techniques on various performance indicators like absolute error, mean average deviation, global absolute error, global mean average deviation, convergence, and computational complexity. Comprehensive Monte Carlo simulations and their statistical analysis are presented to ensure accuracy, consistency in convergence, and computational complexity in terms of execution time. It is observed that the proposed scheme is accurate, reliable, convergent, and computationally viable in treating the nonlinear coupled system under consideration.

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“…Dengue fever is an infectious disease transmitted by mosquito and it occurs in several areas of the world. It causes a high fever and flu symptoms but sometimes it causes a serious bleeding, a sudden drop in blood pressure and death [3,8,16,18]. Dengue fever is most common in Southeast Asia, the western Pacific islands, Latin America and Africa.…”

Section: Introductionmentioning

confidence: 99%

Numerical solution of fractional order SIR model of dengue fever disease via Laplace optimized decomposition method

Maayah,

Bushnaq,

Moussaoui

2023

J. Math. Computer Sci.

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In this research article, we handle the susceptible infected-recovered (SIR) model of the dengue fever epidemic under Caputo Fabrizio fractional derivative. The dengue fever disease is a complicated disease because of the connection it creates between humans and mosquitoes. This encouraged scientists to understand the various factors that influence the recurrence of dengue fever. A new technique called the Laplace Optimized Decomposition (LODM) is used to solve this model numerically and compared with the 4 th order Runge-Kutta Method (RKM). The solution in the proposed method is in the form of a convergent series with easily computable components. We present the solution via graphs and hence give some remarks about the nature of the solutions.

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Analysis and numerical solution of novel fractional model for dengue

Cited by 19 publications

References 16 publications

Numerical Investigations through ANNs for Solving COVID-19 Model

Numerical Investigations through ANNs for Solving COVID-19 Model

Swarm-optimized numerical investigation of Dengue Fever Model

Numerical solution of fractional order SIR model of dengue fever disease via Laplace optimized decomposition method

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