Predictive modeling of hepatitis B viral dynamics: a caputo derivative-based approach using artificial neural...

Area of Science:

• Epidemiology
• Mathematical Biology
• Virology

Background:

• Hepatitis B virus (HBV) poses significant global health and economic burdens.
• Transmission dynamics are complex, involving acute and asymptomatic chronic carrier phases.
• Understanding HBV spread is crucial for effective control strategies.

Purpose of the Study:

• To develop and analyze a novel fractional mathematical model for hepatitis B transmission.
• To incorporate vaccine effects and analyze the model's epidemiological parameters.
• To provide a computational framework for understanding HBV infection dynamics.

Main Methods:

• Developed a fractional hepatitis B virus transmission model using a Caputo derivative.
• Determined the basic reproductive number and analyzed model equilibria using Fixed Point Theory.
• Employed the Adams-Bashforth predictor-corrector scheme for numerical evaluation and Artificial Neural Networks (ANNs) for data analysis.

Main Results:

• Qualitative analysis of the model's approximate solutions was performed.
• Graphical representations illustrated the impact of various fractional orders on transmission dynamics.
• ANN techniques were used for dataset partitioning (training, testing, validation).

Conclusions:

• The fractional HBV model offers a deeper understanding of infection dynamics.
• Mathematical and computational analyses provide insights into HBV transmission.
• Findings can inform the development of targeted public health interventions against HBV.