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Updated: Oct 12, 2025
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Detection of Low Copy Number Integrated Viral DNA Formed by In Vitro Hepatitis B Infection
Published on: November 7, 2018
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A model of hepatitis B virus with random interference infection rate
Dong-Me Li1, Bing Chai1, Qi Wang2
1College of Science, Harbin University of Science and Technology, Harbin 150080, China.
Mathematical Biosciences and Engineering : MBE
|November 24, 2021
Summary
This study models hepatitis B virus (HBV) infection with random factors, finding increased noise reduces drug-resistant viruses. This suggests more frequent HBV DNA testing for accurate diagnosis.
Area of Science:
- Mathematical modeling
- Virology
- Stochastic processes
Background:
- Long-term hepatitis B virus (HBV) treatment can lead to drug resistance.
- Random factors introduce uncertainty into HBV infection rates.
Purpose of the Study:
- To establish a stochastic model for HBV infection rate considering random interference.
- To analyze the asymptotic behavior and stability of the model.
- To investigate the impact of noise intensity on antiviral therapy and drug resistance.
Main Methods:
- Construction of a Lyapunov function.
- Application of Ito's formula for stochastic differential equations.
- Simulation of model dynamics and noise intensity effects.
Main Results:
- The stochastic hepatitis B model is proven to have a unique global positive solution.
- Sufficient conditions for the asymptotic behavior of the solution were determined.
- A direct relationship between noise intensity and oscillation amplitude was established.
- Increased noise intensity was found to decrease the number of drug-resistant viruses.
Conclusions:
- The study validates theoretical findings through simulation, confirming the model's behavior.
- Higher noise intensity may reduce drug-resistant HBV, but impacts diagnostic accuracy.
- Recommendations include increasing the frequency and interval of HBV DNA tests for improved monitoring.
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