使用扩展的剩余功率序列算法,对带有高斯不确定性的分数乙肝模型进行动态分析
Qursam Fatima1, Mubashir Qayyum1, Murad Khan Hassani2
1Department of Sciences and Humanities, National University of Computer and Emerging Sciences, Lahore, Pakistan.
Scientific reports
|March 7, 2025
概括
这项研究通过添加治疗组件并使用模糊数字进行现实的预测来增强乙型肝炎病毒 (HBV) 的数学模型. 新模型改善了对HBV感染动态的理解和策略.
科学领域:
- 流行病学 流行病学
- 数学生物学 数学生物学
- 计算医学是一种计算医学.
背景情况:
- 乙型肝炎病毒 (HBV) 构成了全球健康的重大威胁,导致肝硬化和癌症等严重的肝病.
- 现有的HBV数学模型缺乏详细的治疗组件,限制了它们对治疗干预的预测能力.
研究的目的:
- 扩展目前的乙型肝炎病毒 (HBV) 数学模型,通过整合一个治疗隔间.
- 通过改进的数学建模,增强对HBV感染的理解,诊断和治疗策略.
- 通过解决参数不确定性,开发一个更现实的HBV动态预测框架.
主要方法:
- 对无病平衡点进行了稳定性分析.
- 整合了高斯模糊数以处理参数不确定性并增强模型现实性.
- 扩展的剩余功率序列算法,结合泰勒数列,剩余函数和积分变换,用于解决方案.
- 使用r-cut值评估模型稳定性,图形分析可视化参数影响.
主要成果:
- 拟议的扩展数学模型为预测HBV动态提供了更现实的框架.
- 扩展的剩余功率序列算法有效地解决了模型方程,通过错误计算验证了准确性.
- 图形分析表明了各种参数对疾病进展的影响,为HBV流行病学提供了洞察力.
结论:
- 开发的模糊数学模型为研究乙型肝炎病毒 (HBV) 动态提供了强大而现实的方法.
- 该方法为了解流行病系统提供了新的视角,在生物学,工程和医学领域都有潜在的应用.
- 这项工作有助于改善B型肝炎病毒 (HBV) 感染的诊断和治疗策略.
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