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一种多层神经网络方法用于乙型肝炎模型的稳定性分析

Muhammad Farhan¹, Zhi Ling¹, Zahir Shah²

¹School of Mathematical Science, Yangzhou University, Yangzhou 225002, China.

Computational biology and chemistry

|November 10, 2024

概括

这项研究模拟了乙型肝炎病毒动态,包括无症状携带者和干预措施. 疫苗接种和早期治疗是减少传播和根除乙型肝炎的关键.

科学领域:

流行病学流行病学
数学生物学数学生物学
计算科学计算科学

背景情况:

乙型肝炎病毒 (HBV) 感染是全球主要的健康问题.
乙型肝炎病毒具有显著的风险,有可能从急性感染发展为慢性感染.
了解HBV动态对于有效的公共卫生战略至关重要.

研究的目的:

开发和分析一种用于乙型肝炎病毒感染的新型动态系统.
将无症状携带者,疫苗接种和治疗策略纳入模型.
评估干预措施对HBV传播动态的影响.

主要方法:

利用微分方程系统来建模HBV动态.
计算了用于疾病预测的基本繁殖数 (R0).
采用深度神经网络 (DNN) 来提高模型准确性和预测.
应用Lyapunov函数来确认模型的整体稳定性.
对DNN进行严格的测试,验证和培训.

主要成果:

该模型强调了疫苗接种和早期治疗在缓解HBV传播方面的关键作用.
在各种条件下,DNN在预测系统行为方面表现出很高的准确性.
融合研究,错误分析和回归证实了DNN方法的可靠性.
确定了最优曲线,以最小的剩余误差适应目标解决方案.

结论:

疫苗接种和早期治疗是控制和可能根除乙型肝炎的基本公共卫生工具.
深度神经网络是一种强大的技术,可以提高流行病学建模的准确性.
开发的动态系统为HBV感染动态和干预有效性提供了宝贵的见解.

关键词:

关键的疫苗接种覆盖范围深度神经网络是一种深度神经网络.乙型肝炎病毒的感染.莱文伯格和马奎特的算法.非标准的有限差方法.

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