美国COVID-19死亡率的季节性和周期性模式使用可变带通周期性块启动链
Edward L Valachovic1, Ekaterina Shishova1
1Department of Epidemiology and Biostatistics, School of Public Health, University at Albany, State University of New York, Rensselaer, New York, United States of America.
PloS one
|January 22, 2025
概括
使用新的引导方法调查COVID-19死亡季节性揭示了显著的季节性模式和额外的每周组件. 这种方法为未来的公共卫生准备提供了更准确的预测.
科学领域:
- 流行病学 流行病学
- 生物统计学 生物统计学
- 时间序列分析时间序列分析
背景情况:
- 了解SARS-CoV-2 (导致COVID-19的病毒) 的季节性对于公共卫生准备至关重要.
- 之前分析时间序列数据中的周期组件的方法,如COVID-19死亡率,面临干扰和准确性的挑战.
研究的目的:
- 调查美国COVID-19死亡数据中的季节性和其他定期相关的组件.
- 介绍和评估一种新的启动方法,即可变带通周期块启动,用于分析周期时间序列特征.
主要方法:
- 开发并应用了可变带通周期块启动 (VPBB) 方法.
- 在启动之前,VPBB过时间序列以减少干扰,保留相关性结构.
- 将VPBB与其他启动方法进行比较,以分析美国COVID-19死亡数据.
主要成果:
- 无论是VPBB还是替代方法都确定了COVID-19死亡率中的一个重要的季节性因素.
- 与其他方法相比,VPBB产生了较小的置信区间.
- VPBB在第二到第五和声和一个每周的组件中独特地识别了重要的组件.
结论:
- 可变带通周期块启动是估计时间序列数据中的周期组件的更准确和更强大的统计方法.
- 证据支持在美国COVID-19死亡率中存在显著的季节性模式和额外的周期性成分.
- 研究结果有助于预测和准备未来的COVID-19浪潮,并为公共卫生战略提供信息.
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