一个数据驱动的马尔科夫过程,用于传染性疾病的传播
Chengliang Wang1, Sohaib Mustafa1
1College of Economics and Management, Beijing University of Technology, Beijing, China.
PloS one
|August 10, 2023
概括
一个具有无限状态的新型马尔科夫模型准确地模拟了COVID-19传播动态. 这种传染病模型有助于评估干预策略和了解疫情传播.
科学领域:
- 流行病学 流行病学
- 数学生物学 数学生物学
- 公共卫生 公共卫生
背景情况:
- 2019年冠状病毒大流行 (COVID-19) 引发了全球重大公共卫生和社会经济挑战.
- 了解传染病传播对于制定有效的控制措施至关重要.
- 现有的模型可能无法完全捕捉COVID-19等病毒爆发的复杂性.
研究的目的:
- 提出一种新的数学模型来描述病毒传染性疾病的传播.
- 开发一种用于快速模型分析的模拟方法.
- 评估针对传染病的干预策略的有效性.
主要方法:
- 开发一个具有无限状态空间的水平依赖马尔科夫模型.
- 实施一种包含异质感染的模拟方法.
- 模型验证使用来自约翰霍普金斯大学的COVID-19数据通过MATLAB模拟.
主要成果:
- 拟议的模型有效地捕捉了传染病的传播动态,有或没有干预.
- 模拟结果与现实世界的COVID-19数据保持一致.
- 对模型参数的分析为传输动态提供了洞察力.
结论:
- 水平依赖的马尔科夫模型提供了一个强大的框架,用于研究具有潜在无限感染个体的传染病.
- 该模型有助于评估各种干预策略的影响.
- 这项工作推进了在传染病流行病学中数学建模的理论理解.
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