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滑动模式控制与随机建模和移动性相互作用,用于管理高人口地区的流行病传播
Dewi Suhika1,2, Roberd Saragih3, Dewi Handayani3
1Doctoral Program of Mathematics, Faculty of Mathematics and Natural Sciences, Institut Teknologi Bandung, Ganesha, 10, Bandung 40312, Jawa Barat, Indonesia.
Parasite epidemiology and control
|July 4, 2025
概括
本研究引入了一种使用滑动模式控制 (SMC) 和扩展卡尔曼波器 (EKF) 的新型控制框架,用于管理高流动性地区的传染病传播. 基于模拟的方法有效降低了感染水平,为流行病控制策略提供了洞察力.
科学领域:
- 流行病学和公共卫生.
- 控制系统工程 控制系统工程
- 数学建模的数学建模
背景情况:
- 管理高流动性地区的传染病传播存在重大挑战,原因是复杂的人口互动和传染风险增加.
- 雅加达和西爪等人口密集地区的动态人口流动需要先进的建模和控制策略,以有效管理疫情.
研究的目的:
- 开发一种随机流行病学模型来模拟印尼雅加达和西爪之间的疾病传播.
- 提出并整合一个滑动模式控制 (SMC) 框架与扩展卡尔曼波器 (EKF) 实时参数估计和控制分析在不确定性.
- 用理论和基于模拟的方法评估疫苗接种和隔离策略对传染病传播的潜在影响.
主要方法:
- 开发一个随机流行病学模型来模拟疾病传播动态.
- 集成了强大的滑动模式控制 (SMC) 框架与扩展卡尔曼波器 (EKF) 集成,用于从有限的可观测数据中进行参数估计.
- 在不确定性和随机性条件下模拟疾病传播和控制策略的有效性.
主要成果:
- 拟议的SMC战略显著降低了感染水平:在雅加达分别为84.45%和63.94%,在西爪分别为98.83%和58.35%的原始和Omicron变种.
- EKF成功估计了无法观察到的流行病学参数,使得尽管状态变量测量不完整,但能够进行对照分析.
- 随机模型有效地捕捉了流行病进展中固有的自然波动和不确定性.
结论:
- 集成的EKF和SMC框架为管理复杂环境中的随机流行系统提供了一个概念工具.
- 虽然无法直接实现实时政策,但数据驱动的控制模拟为疾病动态和战略影响提供了宝贵的见解.
- 这些发现支持在不确定的流行病环境中使用基于模拟的控制来评估场景和提供政策指导.
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