在无症状患者感染性疾病的马尔科夫建模和性能分析
Quan-Lin Li1, Chengliang Wang1, Feifei Yang1
1School of Economics and Management, Beijing University of Technology, Beijing 100124, China.
Mathematical biosciences and engineering : MBE
|December 5, 2023
概括
这项研究引入了一种新的马尔科夫过程模型,以了解传染病的传播,特别是来自无症状携带者. 该模型有助于分析疾病传播动态和长期公共卫生控制策略.
科学领域:
- 流行病学和生物统计学
- 传染病的数学建模 传染病的数学建模
- 随机过程 随机过程
背景情况:
- 传染病构成了持续的威胁,根除因症状前和无症状传播而复杂化.
- 涉及无症状个体的长期传播动态仍然不完全理解,这阻碍了有效控制.
- 目前的模型可能无法完全捕捉无症状传染病携带者带来的复杂性.
研究的目的:
- 开发一种用于分析传染病传播的新型数学模型,特别包括无症状患者.
- 为了推导出所提出的传染病马尔科夫过程模型的稳定性条件.
- 为计算与疾病传播和控制相关的关键绩效指标提供方法.
主要方法:
- 开发一个连续的时间,水平依赖的准出生和死亡 (QBD) 过程,以模拟传染病的传播.
- 应用平均漂移技术来建立马尔科夫过程的稳定性条件.
- 使用 RG 分因子计算 QBD 过程的静止概率向量.
主要成果:
- 一个新的马尔科夫过程模型准确地捕捉了传染病传播动态,包括无症状病例.
- 导出了模型稳定性的足够条件,有助于分析疾病持久性.
- 静态概率向量为计算疾病管理的关键性能指标提供了基础.
结论:
- 开发的QBD模型为研究无症状携带者长期传播传染病提供了强大的框架.
- 以COVID-19为例的数值实验证明了模型的可行性和关键参数的影响.
- 这项研究为科学控制策略提供了理论和技术支持,以应对持续存在的传染病威胁.
关键词:
马尔科夫过程是一个马尔科夫过程.没有症状的患者.冠状病毒疾病2019 (COVID-19) 是一种新型冠状病毒疾病.绩效评价 绩效评价 绩效评价 绩效评价 绩效评价准出生和死亡过程 (QBD)更多相关视频
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