在疾病建模中的记忆效应通过核心估计与振荡时间历史的估计.
Adam Mielke1, Mads Peter Sørensen2, John Wyller3
1Department of Applied Mathematics and Computer Science, Technical University of Denmark, Richard Petersens Plads, Bldg. 324, 2800, Kongens Lyngby, Denmark.
Journal of mathematical biology
|April 5, 2024
概括
这项研究引入了一种分析疾病传播的新算法,表明像社交距离这样的适应性行为可以稳定人口和降低感染率.
科学领域:
- 传染病的数学建模 传染病的数学建模
- 动态系统理论 动态系统理论
- 流行病学 流行病学
背景情况:
- 传染病模型往往简化了人口的行为.
- 分布的时间延迟和记忆效应对于现实的疾病动态至关重要.
- 适应性行为,包括非药物干预,显著影响疾病传播.
研究的目的:
- 开发一种新的算法框架,用于分析具有分布式时间延迟的动态系统.
- 用SIR和SEIR模型研究人口级疾病演变中的记忆效应.
- 分析适应性行为对疾病动态的稳定性和影响.
主要方法:
- 为具有振荡时间历史的动态系统设计线性链算法.
- 将算法应用于易感感染者-康复者 (SIR) 和易感暴露者-感染者-康复者 (SEIR) 模型.
- 在使用历史依赖内核的自适应行为下分析系统稳定性和攻击率.
主要成果:
- 线性链技巧将延迟模型转换为马科维系统.
- 适应性行为可以导致稳定的平衡或稳定的极限周期.
- 该模型显示,与非适应性场景相比,攻击率降低.
结论:
- 通过历史依赖的核心建模的适应性行为可以稳定疾病动态.
- 拟议的算法有效地分析了流行病学模型中的记忆效应.
- 虽然适应性行为提供了短期的好处,但随着疾病的消退,其长期影响会减少.
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