以和发病率和非线性恢复率为基础的基于网络的SIR模型的动态分析:边缘分隔式方法
Fang Wang1,2, Juping Zhang3, Maoxing Liu1,4
1School of Big Data, North University of China, Taiyuan 030051, China.
Mathematical biosciences and engineering : MBE
|June 14, 2024
概括
维持足够的医院床位对于控制传染病至关重要. 这项研究表明,仅仅较低的基本生殖数量并不能保证疾病的根除,而不足的床位可能会引发向后分叉.
科学领域:
- 流行病学 流行病学
- 数学建模的数学建模
- 网络科学 网络科学
背景情况:
- 传统的SIR模型往往简化了疾病传播动态.
- 网络结构和和发病率/非线性恢复率显著影响流行病的传播.
- 了解无疾病和特有平衡稳定性对于公共卫生干预至关重要.
研究的目的:
- 提出和分析一种基于网络的新型SIR流行病模型.
- 调查和发病率和非线性恢复对疾病动态的影响.
- 确定全球非对称稳定性和多重平衡存在的条件.
主要方法:
- 开发了一种边缘-分隔式方法来制定一个度边缘混合模型.
- 使用更新方程和拉普拉斯转换来导出基本复制数 ($R_0$).
- 分析了均衡的局部和全球稳定性,并研究了分叉现象.
主要成果:
- 确定 $R_0 < 1$ 对于无疾病平衡的全球非对称稳定性是不够的.
- 证明了当$R_0>1$时,多种特有平衡的潜力.
- 在有限的医院床位容量条件下,在$R_0 = 1$时确定了向后分叉.
结论:
- 流行病平衡的稳定性取决于流行病曲线的触角斜率.
- 医院床位有限可能导致复杂的动态,包括向后分叉,阻碍疾病控制.
- 维持足够的医院病床容量是有效传染病管理的关键因素.
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