统一发生率和流行率在一个随时间变化的一般分支过程下
Mikko S Pakkanen1,2, Xenia Miscouridou3, Matthew J Penn4
1Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada. m.pakkanen@imperial.ac.uk.
Journal of mathematical biology
|August 1, 2023
概括
这项研究引入了一个新的随机爆发模型,使用时间变化的分支过程. 该模型准确地从真实世界的传染病数据中估计了传染率.
科学领域:
- 流行病学 流行病学
- 数学生物学 数学生物学
- 传染病的动态传染病的动态.
背景情况:
- 更新方程是模拟传染病发病率的标准.
- 现有的模型通常假定常数参数,限制它们对现实世界爆发的适用性,变化的动态.
研究的目的:
- 为传染病爆发开发一种灵活的随机模型,以适应时间变化的参数.
- 在这个新的框架内,为发病率和流行率推导和分析类似于更新的整方程.
- 用历史和当前的流行病学数据验证该模型估计传播率的能力.
主要方法:
- 开发了一个时间变化的Crump-Mode-Jagers分支过程模型.
- 为发病率,累积发病率和流行率推导出类似更新的整方程.
- 分析了包括贝尔曼-哈里斯和不均的波桑过程在内的具体案例.
- 实现了一个数值离散方案来解决衍生方程.
主要成果:
- 发病率和流行率的演算方程与后续计算关系保持一致.
- 该模型的发病率方程与传染病建模中广泛使用的更新方程一致.
- 成功估计了英国的SARS-CoV-2传播率以及流感,麻疹,SARS和天花的历史数据.
结论:
- 开发的随机分支过程模型为分析具有时间变化的传播动态的传染病爆发提供了强大的框架.
- 由此得出的积分方程和数值方法可以从流行病学数据中准确估计传播率.
- 这种方法提高了我们对疾病传播的理解,并为公共卫生干预提供了信息.
关键词:
背向计算的背向计算分支的过程分支过程.在 COVID-19 疫情中,Crump 模式 Jagers 的过程.影响影响 影响 影响不同质的波桑过程流行程度 流行程度更新方程式的更新方程式复制编号复制编号时间变化的复制号码.更多相关视频
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