估计流行病的指数增长率的估计
Manting Wang1, P van den Driessche1, Laura L E Cowen1
1Department of Mathematics and Statistics, University of Victoria, Victoria, BC, V8W 2Y2, Canada.
Infectious Disease Modelling
|February 18, 2026
概括
隐藏的马尔科夫模型 (HMM) 提供比标准回归更可靠的流行病增长率估计. 这个框架,扩展了一个物流模型,提高了早期COVID-19大流行数据的稳定性.
科学领域:
- 流行病学 流行病学
- 数学生物学 数学生物学
- 统计建模 统计建模
背景情况:
- 准确估计最初的流行病增长率对于公共卫生干预至关重要.
- 标准回归方法可能会低估由于独立性假设的不确定性.
- 没有观察到的传染性人群在流行病建模中构成了挑战.
研究的目的:
- 提出一种隐藏的马尔科夫模型 (HMM) 框架,用于对流行病初始增长率的可靠估计.
- 使用模拟数据,比较HMM与负二项式回归的性能.
- 将HMM框架扩展为后指数增长阶段的后勤模型.
主要方法:
- 开发了一个隐藏的马尔科夫模型 (HMM) 框架,以明确模拟未被观察到的传染病群体.
- 利用随机线性SEIR模型的数据进行性能比较.
- 与HMM集成了一个物流模型,以捕捉从指数增长到较慢增长的过渡.
- 将扩展的HMM-物流模型应用于来自非洲和安大略省的早期COVID-19数据.
主要成果:
- 与负二项式回归相比,HMMs对指数式增长率的估计表现出了更强大和可靠的估计.
- HMM显示了95%可信度间隔的更好的覆盖概率.
- 在HMM框架内以负二项式或二项式分布建模感染群体,可以得出更准确的推理.
- 扩展的HMM-物流框架提高了对真实世界COVID-19数据估计的稳定性和可靠性.
结论:
- 拟议的HMM框架对估计初始流行病增长率的标准方法提供了显著的改进.
- 这种HMM-logistic扩展有效地模拟了疫情动态,超出了最初的指数阶段.
- 这种方法提供了更稳定和可靠的估计,对于指导公共卫生对新出现的传染病的反应至关重要.
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