在流行病学监测中,主要流行病时间序列之间的时间扭曲
Jean-David Morel1, Jean-Michel Morel2, Luis Alvarez3
1Laboratoire de Physiologie Intégrative et Systémique, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
PLoS computational biology
|December 27, 2023
概括
流行病学监测数据,就像新增病例和死亡数据一样,由一个通用的更新方程联系在一起. 一种新的信号处理方法精确计算了这些健康指标之间的时间变化的延迟和比率.
科学领域:
- 流行病学 流行病学
- 数学生物学 数学生物学
- 信号处理 信号处理
背景情况:
- 流行病学监测通常跟踪每天/每周的新病例,住院,ICU住院和死亡.
- 这些时间序列对于监测COVID-19大流行至关重要.
- 这些流行病曲线之间的关系往往是复杂的,需要先进的分析方法.
研究的目的:
- 建立一个通用的更新方程,将流行病时间序列的对联系起来.
- 开发一种有效的数值方法,用于计算不同地区或不同国家的同时健康指标之间的时间变化的延迟和比率.
- 用模拟和现实数据验证方法的准确性和一致性.
主要方法:
- 使用信号处理方法来开发数值方法.
- 该方法计算时间变化的延迟和时间序列对之间的比率曲线.
- 该方法经过对称性和过渡性质的测试,并通过模拟数据进行验证.
主要成果:
- 流行病时间序列的对与时间变化的延迟和比率的概括更新方程有关.
- 开发的数值方法准确计算了这些随时间变化的参数.
- 该方法证明了一致性,对称性和过渡性,并在模拟中忠实地恢复了参数.
- 现实世界的例子显示了可解释的延迟和比率.
结论:
- 拟议的方法为分析多个流行病学时间序列之间的关系提供了一个统一的框架.
- 它通过量化系列间的关系来增强对跨地区和时间的流行病动态的理解.
- 该方法概括并改进了现有的健康数据分析方法.
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