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<p>Hepatitis B virus (HBV) is a significant global health concern, causing acute and chronic liver diseases, including cirrhosis and hepatocellular carcinoma. This manuscript extends existing mathematical models for HBV by introducing a treatment compartment to improve understanding, diagnosis, and treatment strategies. A stability analysis is conducted for disease-free equilibrium and to address the inherent uncertainties in parameter values, Gaussian fuzzy numbers are incorporated, resulting in a more realistic predictive framework. For solution purposes, the extended residual power series algorithm, which combines the Taylor series with a residual function and an integral transform, is applied. The accuracy of the obtained solutions is assessed by calculating the associated errors. The robustness of the model is further evaluated using <i>r</i>-cut values for lower and upper bounds.A graphical analysis is also performed to examine the influence of different parameters on the solution profiles, enhancing the understanding of disease dynamics. The analysis reveals that the proposed methodology effectively explains the dynamics of epidemic systems and provides new perspectives with potential applications in biology, engineering, and medicine.</p>

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Dynamical analysis of fractional hepatitis B model with Gaussian uncertainties using extended residual power series algorithm

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月7日

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  • Qursam Fatima,
  • Mubashir Qayyum,
  • Murad Khan Hassani,
  • Ali Akgül

摘要

Hepatitis B virus (HBV) is a significant global health concern, causing acute and chronic liver diseases, including cirrhosis and hepatocellular carcinoma. This manuscript extends existing mathematical models for HBV by introducing a treatment compartment to improve understanding, diagnosis, and treatment strategies. A stability analysis is conducted for disease-free equilibrium and to address the inherent uncertainties in parameter values, Gaussian fuzzy numbers are incorporated, resulting in a more realistic predictive framework. For solution purposes, the extended residual power series algorithm, which combines the Taylor series with a residual function and an integral transform, is applied. The accuracy of the obtained solutions is assessed by calculating the associated errors. The robustness of the model is further evaluated using r-cut values for lower and upper bounds.A graphical analysis is also performed to examine the influence of different parameters on the solution profiles, enhancing the understanding of disease dynamics. The analysis reveals that the proposed methodology effectively explains the dynamics of epidemic systems and provides new perspectives with potential applications in biology, engineering, and medicine.