在SIRS模型中,非线性发病率和双重暴露的全球分支动态
Yancong Xu1, Yue Yang2, Malay Banerjee3
1Department of Mathematics, China Jiliang University, Hangzhou, 310018, China.
传染病的数学模型揭示了复杂的动态. 传播和免疫的非线性可以导致多种疾病状态和突发的疫情模式转变.
科学领域:
- 流行病学 流行病学
- 数学生物学 数学生物学
- 动态系统理论 动态系统理论
背景情况:
- 数学建模对于了解传染病传播至关重要.
- 易受感染-恢复-易受感染 (SIRS) 模型被广泛用于研究疾病动态.
- 非线性发病率函数可以在流行病学模型中引入复杂的行为.
研究的目的:
- 分析SIRS模型的全局动态,使用一般化的非线性发病率函数.
- 识别和描述各种分叉现象.
- 探索多个极限周期对疾病持续性和爆发模式的影响.
主要方法:
- 全球分支分析全球分支分析
- 正常形式理论是正常形式理论.
- 一步转换方法的一步转换方法.
- 对二维和三维博格达诺夫-塔肯斯分叉的分析.
主要成果:
- SIRS模型展示了丰富多样的分叉,包括Hopf,Bogdanov-Takens和隔离分叉.
- 霍夫分叉可以达到共维三,导致多达三个小幅度极限周期.
- 极限周期的共存导致双稳定和三稳定动态,在双重暴露模型中发现了极限周期的孤立.
- 传播和免疫的非线性可以导致反复爆发或持续感染.
结论:
- 在SIRS模型中的非线性发病率函数产生复杂的动态和多个稳定的疾病状态.
- 多个极限周期的存在强调了由于小参数变化导致疾病动态突然转变的可能性.
- 研究结果强调需要适应性公共卫生战略,以有效管理传染病.
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