一种基于梯度的时间延迟优化算法,用于评估分数顺序传染病模型中的控制策略
Indranil Ghosh1, Huey Tyng Cheong1, Kok Lay Teo1
1School of Mathematical Sciences, Sunway University, 47500 Selangor Darul Ehsan, Malaysia.
这项研究优化了使用分数顺序模型的传染病控制策略,实现了显著的成本降低. 这些发现证明了实施有针对性的干预措施的财务效益,并强调了记忆效应在疾病建模中的重要性.
科学领域:
- 流行病学 流行病学
- 数学生物学 数学生物学
- 控制理论 控制理论
背景情况:
- 数学模型对于疾病控制战略的制定至关重要.
- 优化具有成本效益的干预措施仍然是一个重大挑战.
- 现有的模型往往缺乏现实的成本函数和时间依赖的参数优化.
研究的目的:
- 为了优化SIDARTHE模型,为最佳的干预策略提供成本和控制功能.
- 开发和应用一种新的基于梯度的时间延迟优化算法来降低成本.
- 调查分数顺序导数和记忆特性对疾病建模的影响.
主要方法:
- 改进了具有成本和控制功能的八个分区SIDARTHE模型.
- 应用Adams-Bashforth方法和复合梯形规则进行优化.
- 实施卡普托-法布里齐奥分数导数来建模疾病动态.
主要成果:
- 通过优化控制干预措施实现了大约35.61%的成本降低.
- 证明了实施控制策略的财务效益.
- 使用卡普托-法布里齐奥分数导数,特别是分数顺序为0.7,与意大利疾病数据相匹配,展示了改进的模型准确性.
结论:
- 开发的时间延迟优化算法有效降低了与疾病控制相关的成本.
- 分数顺序建模,特别是使用卡普托-法布里齐奥衍生品,通过捕捉记忆效应来提高准确性.
- 这项研究为未来优化传染病治疗策略的研究提供了基础.
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