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使用反线性化和状态估计的非线性流行病学模型进行强有力的控制和数据重建
Balázs Csutak1, Gábor Szederkényi1,2
1Faculty of Information Technology and Bionics, Pázmány Péter Catholic University, Práter u. 50/A, H-1083, Budapest, Hungary.
Mathematical biosciences and engineering : MBE
|February 14, 2025
概括
控制理论为流行病学建模提供了一个强大的框架. 本研究提出了一种计算方法,用于疫情模型中的状态估计和数据重建,即使在不确定性的情况下,也证明了有效的控制.
科学领域:
- 流行病学 流行病学
- 控制理论 控制理论
- 计算生物学 计算生物学
背景情况:
- 控制理论为复杂的流行病学任务提供了有效的框架.
- 非线性分区流行病模型对于理解疾病动态至关重要.
- 国家估计和历史数据重建对于流行病管理至关重要.
研究的目的:
- 为状态估计和参考跟踪控制提出一个计算方法.
- 使用非线性分区模型重建历史流行病数据.
- 在模型和参数不匹配的情况下评估控制策略的稳定性.
主要方法:
- 使用非线性输入-同源控制模型,以疾病传播率作为输入.
- 采用了一种具有反线性化的易受-暴露-感染-恢复 (SEIR) 模型.
- 集成了一个扩展的卡尔曼过器用于状态估计,比较不同的信息可用性场景.
主要成果:
- 通过使用瑞典和匈牙利的流行病数据,证明了成功的输出跟踪和历史数据重建.
- 展示了尽管存在显著的模型和参数不确定性,但拟议的控制方法的有效性.
- 证实,精心设计的反可以显著减轻建模错误的影响,即使有观察不确定性.
结论:
- 拟议的计算方法为流行病控制和数据重建提供了一个强大的方法.
- 反线性化和扩展的卡尔曼过是管理流行病模型的有效工具.
- 控制策略证明了对不确定性的弹性,突出了其在现实世界的场景中的实际适用性.
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