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Updated: Jul 4, 2026
09:02
Modeling Hepatitis B Virus Infection in Non-Hepatic 293T-NE-3NRs Cells
Published on: June 5, 2020
Hepatitis B virus spreading via Beddington-DeAngelis incidence function and feed-forward neural network with optimal
Tahir Khan1, Shehla Ibrar2, Yunil Roh3
1Department of Mathematics/Institute of Mathematical Sciences, Pusan National University, Busan, South Korea.
Journal of Biological Dynamics
|July 2, 2026
Summary
This study models hepatitis B virus (HBV) transmission dynamics and optimal control using mathematical and hybrid neural network methods. It identifies key parameters and verifies control strategies to minimize infections and maximize recovery.
Area of Science:
- Epidemiology
- Mathematical Biology
- Computational Science
Background:
- Hepatitis B virus (HBV) poses a significant global health challenge.
- Mathematical modeling is crucial for understanding disease transmission dynamics.
- Integrating epidemiological models with computational methods enhances predictive capabilities.
Purpose of the Study:
- To analyze the transmission dynamics of hepatitis B virus (HBV) using a mathematical model.
- To determine optimal control strategies for mitigating HBV spread.
- To validate theoretical findings using a hybrid computational approach.
Main Methods:
- Development of a mathematical model incorporating the Beddington-DeAngelis incidence function.
- Analysis of model well-posedness and stability using a threshold parameter.
- Application of a hybrid 4th order Runge-Kutta (RK4) and feed-forward neural network (FFNN) method for simulation and control verification.
Main Results:
- Quantification of sensitive epidemic parameters and their impact on HBV transmission.
- Formulation of an optimal control problem to minimize infected populations and maximize recovered individuals.
- Successful approximation of temporal HBV dynamics and validation of control effects using the hybrid RK4-FFNN method.
Conclusions:
- The study provides insights into HBV transmission dynamics and effective control strategies.
- Hybrid computational methods offer a robust approach for analyzing and managing infectious diseases.
- The findings support the development of targeted interventions to control HBV outbreaks.
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