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<p>The aim of the work is to solve the fractional order infectious disease model with the awareness and vaccination effects by executing reliable neural network strategies. Fractional kinds of derivatives perform higher efficiency and accuracy in comparison with the derivatives of integer kinds. The fractional order infectious disease model with the awareness and vaccination effects is separated into susceptible class, vaccinated class, infected class, quarantined class, and removed class. A construction of the proposed neural network is accomplished by a single layer construction with log-sigmoid transfer function together with 24 neurons. The model is trained using the Adam optimizer along with the Bayesian regularization, a reliable solver to perform the results of nonlinear systems. The dataset obtained between 0 and 1 with the step size of 0.01, which is divided into three states: validation 10%, training 76%, and testing 14%. The numerical solutions of the infectious disease model with the awareness and vaccination effects are performed by taking three fractional order cases between 0 and 1. The solver’s exactness is perceived by the identical solutions, optimal training and absolute error. The reliability of the proposed algorithm is achieved based on regression coefficients, state transitions, and best fitness.</p>

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Fractional-order modeling of infectious diseases: a stochastic neural network procedure to deal with vaccination and awareness strategies

  • 期刊论文

  • 发表日期：

    2026年2月2日

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  • Muhammad Umar,
  • Soheil Salahshour,
  • Nancy Akoum,
  • Waqar Ishaq,
  • Umida Baltaeva

摘要

The aim of the work is to solve the fractional order infectious disease model with the awareness and vaccination effects by executing reliable neural network strategies. Fractional kinds of derivatives perform higher efficiency and accuracy in comparison with the derivatives of integer kinds. The fractional order infectious disease model with the awareness and vaccination effects is separated into susceptible class, vaccinated class, infected class, quarantined class, and removed class. A construction of the proposed neural network is accomplished by a single layer construction with log-sigmoid transfer function together with 24 neurons. The model is trained using the Adam optimizer along with the Bayesian regularization, a reliable solver to perform the results of nonlinear systems. The dataset obtained between 0 and 1 with the step size of 0.01, which is divided into three states: validation 10%, training 76%, and testing 14%. The numerical solutions of the infectious disease model with the awareness and vaccination effects are performed by taking three fractional order cases between 0 and 1. The solver’s exactness is perceived by the identical solutions, optimal training and absolute error. The reliability of the proposed algorithm is achieved based on regression coefficients, state transitions, and best fitness.