โก Quick Summary
This study introduces a novel framework combining stochastic modeling and feed-forward neural networks (FFNN) to analyze the dynamics of hepatitis B virus (HBV) transmission. The approach effectively captures the inherent uncertainty of disease spread, demonstrating strong predictive performance through real data simulations.
๐ Key Details
- ๐ Dataset: Real data of hepatitis B reported cases
- ๐งฉ Features used: Stochastic differential equations and neural network dynamics
- โ๏ธ Technology: Feed-forward neural network (FFNN)
- ๐ Performance metrics: Mean squared error (MSE) and absolute error (AE)
๐ Key Takeaways
- ๐ Global health burden: Nearly 2 billion infections of hepatitis B worldwide.
- ๐ฌ Novel framework: Combines stochastic modeling with FFNN for enhanced analysis.
- ๐ Strong predictive performance: Demonstrated through regression analysis and error metrics.
- ๐งฉ Captures complexity: Effectively models the uncertainty in disease transmission.
- ๐ Theoretical analysis: Proven well-posedness and conditions for disease extinction and persistence.
- ๐ค Hybrid approach: Integrates real data with advanced computational techniques.
- ๐ฅ Implications for public health: Potential to inform vaccination strategies and disease management.
๐ Background
Hepatitis B remains a significant global health challenge, with its transmission influenced by various factors such as environmental heterogeneity and host-pathogen interactions. Understanding the dynamics of HBV is crucial for developing effective interventions and vaccination strategies. Traditional models often struggle to capture the complexity and uncertainty inherent in disease spread, highlighting the need for innovative approaches.
๐๏ธ Study
The researchers aimed to develop a comprehensive model that accurately reflects the dynamics of hepatitis B transmission. By formulating a stochastic model with a saturated incidence rate, they incorporated key characteristics of disease persistence. The study utilized real data to train a feed-forward neural network, allowing for more effective approximation of model dynamics.
๐ Results
The hybrid framework demonstrated impressive performance, with metrics such as mean squared error (MSE) and absolute error (AE) indicating strong agreement between the stochastic simulations and neural network predictions. The theoretical analysis confirmed the model’s well-posedness and established conditions for both extinction and persistence of the disease, underscoring its robustness.
๐ Impact and Implications
This study’s findings have significant implications for public health, particularly in the context of hepatitis B management. By leveraging advanced computational techniques, the model can enhance our understanding of disease dynamics, potentially guiding vaccination strategies and informing public health policies. The integration of real data with sophisticated modeling approaches paves the way for more effective interventions in combating hepatitis B.
๐ฎ Conclusion
The research highlights the potential of combining stochastic modeling with neural networks to address complex health challenges like hepatitis B transmission. This innovative approach not only improves our understanding of disease dynamics but also offers a promising avenue for future research and public health applications. Continued exploration in this field could lead to breakthroughs in managing infectious diseases more effectively.
๐ฌ Your comments
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Numerical computation of the stochastic hepatitis B model using feed forward neural network and real data.
Abstract
Hepatitis B is a global health burden and can persist for years, with nearly two billion infections worldwide, where its spread is influenced by environmental heterogeneity, host-pathogen interactions, and vaccination-induced immune variability. Proper understanding and developing models with a suitable framework is essential to accurately capture the complexity of the hepatitis B virus (HBV) and its transmission. In this work, we present a novel framework of a stochastic model and a forward neural network that combines neural networks and stochastic differential equations to analyze the dynamics of hepatitis B virus transmission, as it is important to capture the inherent uncertainty of disease spread in heterogeneous environments. We formulate the stochastic model with a saturated incidence rate, incorporating the long-term persistence of the disease following key characteristics of the disease transmission. The theoretical analysis of the model is proven to ensure the well-posedness and to determine the conditions for extinction and persistence of the disease. Further, a set of real data of hepatitis B reported cases will be used to produce stochastic simulations, and to train a feed-forward neural network (FFNN), while approximating the model dynamics more effectively. To evaluate the efficacy of the hybrid framework, we demonstrate its performance by the presenting mean squared error (MSE), absolute error (AE), and regression analysis showing strong agreement between the stochastic simulations and neural network predictions.
Author: [‘Khan T’, ‘Jung IH’]
Journal: Sci Rep
Citation: Khan T and Jung IH. Numerical computation of the stochastic hepatitis B model using feed forward neural network and real data. Numerical computation of the stochastic hepatitis B model using feed forward neural network and real data. 2025; 15:43858. doi: 10.1038/s41598-025-27628-z