是否通过考虑非线性预测控制来制SARS-CoV-2变种的传播?
Mohadeseh Najafi1, Hamidreza Mortazavy Beni2, Ashkan Heydarian3
1Department of Electrical and Computer Engineering, Hakim Sabzevari University, Sabzevar, Iran.
Biomedical engineering and computational biology
|April 28, 2025
概括
这项研究使用分数计算和非线性模型预测控制 (NMPC) 来建模SARS-COV-2 (COVID-19). NMPC有效地减少了敏感人群的预测错误,为流行病控制提供了一个新的工具.
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:
- SARS-COV-2 (COVID-19) 的持续影响需要先进的建模技术.
- 传统的流行病模型可能无法完全捕捉传染病的复杂性和长期动态.
- 检疫等控制措施的影响需要强大的分析框架.
研究的目的:
- 为了研究SARS-COV-2流行病模式,使用分数顺序的数学建模.
- 评估非线性模型预测控制器 (NMPC) 对于流行病监测和控制的有效性.
- 将检疫的影响纳入小数顺序模型.
主要方法:
- 为SARS-COV-2传播动态开发一个分数级数学模型.
- 集成非线性模型预测控制器 (NMPC) 用于实时监控和预测.
- 模拟分析将拟议的NMPC与分数顺序的最佳控制进行比较.
主要成果:
- 分数顺序模型捕捉了内存和遗传性质,提供了增强的参数调节性.
- 拟议的NMPC在预测易感个体时显示出明显较低的平均平方误差 (~3.6e-04) 与分数顺序的最佳控制相比 (47.4).
- 模拟证实了NMPC预测和管理未来流行病状况的能力.
结论:
- 分数顺序建模与NMPC相结合,为分析和控制流行病提供了强大的方法.
- 开发的NMPC是减少疫情期间敏感人群预测错误的有希望的工具.
- 该方法可适应用于其他传染病模型.
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