确定性和随机性SEIR模型的动态分析,包括奥恩斯坦-乌伦贝克过程
Pritam Saha1, Kalyan Kumar Pal2, Uttam Ghosh1
1Department of Applied Mathematics, University of Calcutta, Kolkata 700009, India.
Chaos (Woodbury, N.Y.)
|February 26, 2025
概括
这项研究引入了易受-暴露-感染-恢复 (SEIR) 模型来分析疾病动态. 研究结果显示,当基本繁殖数 (R0) 小于 1 时,疾病的根除发生,当 R0 大于 1 时,持续性发生.
科学领域:
- 流行病学 流行病学
- 数学生物学 数学生物学
- 动态系统 动态系统
背景情况:
- 流行病建模对于了解疾病传播至关重要.
- 现有的模型往往简化了复杂的疾病动态.
- 结合随机性和现实的参数可以提高模型的准确性.
研究的目的:
- 开发和分析一种新的易受-暴露-感染-恢复 (SEIR) 流行病模型.
- 调查SEIR模型的决定性和随机版本.
- 评估模型参数和行为的流行病学影响.
主要方法:
- 开发一种具有非线性发病率和和处理的确定性SEIR模型.
- 使用Ornstein-Uhlenbeck过程对随机SEIR模型的分析.
- 均衡的稳定性分析和分叉现象的调查 (跨临界,向后,结,Hopf).
- 莱帕努诺夫函数用于随机模型分析的应用.
主要成果:
- 疾病根除 (R0<1) 和持续 (R0>1) 的确定的条件.
- 证明了对随机模型有一个独特的全球积极解决方案的存在.
- 在随机模型中确定了疾病灭绝和持续存在的足够条件.
- 确定了随机模型的静态分布的存在.
- 通过数值模拟验证了理论发现.
结论:
- 该SEIR模型准确地捕捉了在恒定和噪音环境中的疾病动态.
- 分叉分析揭示了复杂的流行病学行为.
- 随机性在疾病的持续性和灭绝中发挥着重要作用.
- 该模型为公共卫生干预提供了有价值的见解.
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