Modeling Fractional-Order Tuberculosis Epidemics Using SL-BRNN

Zulqurnain Sabir, Zafar Duman Abbasov, Nabila Brahmi Chendouh and A. M. Shloof

Complexity, 2026, vol. 2026, 1-11

Abstract: The present research provides the solutions of the fractional order (FO) tuberculosis (TB) epidemics model by applying the supervised learning Bayesian regularization neural network (SL-BRNN). The Caputo fractional derivative is executed to solve the TB epidemic model, which is essential to perform the intricate systems with nonlinear relations and delivers a powerful mathematical structure for relating systems with FO dynamics. The model is described into six population groups: susceptible, latent, treatment of Type 1 and 2, infected, and recovered. A construction of network is performed by taking sigmoid activation function and 21 neurons in the hidden layer, 1000 epochs, and optimization through BR. The dataset of the FO-TB epidemics model is calculated using the Adams–Bashforth–Moulton technique in intervals 0 and 1, with a 0.01 step size, which reduces the mean square error by data splitting into training (73%), testing (14%), and validation (13%). Moreover, the data training is performed using the designed construction of the solver based on SL-BRNN. The solutions of three FO-TB epidemics models are presented in three cases by taking the FO derivative values 0.7, 0.8, and 0.9. In addition, the solutions of the proposed SL-RNN are compared with the Levenberg–Marquardt backpropagation algorithm in order to check the accuracy of the scheme. Moreover, the best training values, error histogram, fitness performances, absolute error, and state transition further provide the accuracy and reliability of the designed SL-BRNN.

Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:9424770

DOI: 10.1155/cplx/9424770

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