Valachovic Edward L, Shishova Ekaterina
Department 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. 2025 Jan 22;20(1):e0317897. doi: 10.1371/journal.pone.0317897. eCollection 2025.
Since the emergence of the SARS-CoV-2 virus, research into the existence, extent, and pattern of seasonality has been of the highest importance for public health preparation. This study uses a novel bandpass bootstrap approach called the Variable Bandpass Periodic Block Bootstrap to investigate the periodically correlated components including seasonality within US COVID-19 mortality. Bootstrapping to produce confidence intervals for periodic characteristics such as the seasonal mean requires preservation of the periodically correlated component's correlation structure during resampling. While other existing bootstrap methods can preserve the periodically correlated component correlation structure, filtration of that periodically correlated component's frequency from interference is critical to bootstrap the periodically correlated component's characteristics accurately and efficiently. The Variable Bandpass Periodic Block Bootstrap filters the periodically correlated time series to reduce interference from other components such as noise. This greatly reduces bootstrapped confidence interval size and outperforms the statistical power and accuracy of other methods when estimating the periodic mean sampling distribution. Variable Bandpass Periodic Block Bootstrap analysis of US COVID-19 mortality periodically correlated components is provided and compared against alternative bootstrapping methods. Results show that both methods find a significant seasonal component, but the Variable Bandpass Periodic Block Bootstrap produces smaller confidence intervals and only the Variable Bandpass Periodic Block Bootstrap found significant components at the second through the fifth harmonics of the seasonal component, as well as weekly component. This crucial evidence supporting the presence of a seasonal pattern and existence of additional periodically correlated components, their timing, and confidence intervals for their effect which will aid prediction and preparation for future COVID-19 responses.
自严重急性呼吸综合征冠状病毒2（SARS-CoV-2）病毒出现以来，对季节性的存在、程度和模式进行研究对于公共卫生准备工作至关重要。本研究采用一种名为可变带通周期块自抽样法的新型带通自抽样方法，来调查美国新冠肺炎死亡率中包括季节性在内的周期性相关成分。通过自抽样来生成诸如季节均值等周期性特征的置信区间，这需要在重新抽样过程中保留周期性相关成分的相关结构。虽然其他现有的自抽样方法可以保留周期性相关成分的相关结构，但从干扰中过滤该周期性相关成分的频率对于准确有效地自抽样该周期性相关成分的特征至关重要。可变带通周期块自抽样法对周期性相关时间序列进行滤波，以减少来自噪声等其他成分的干扰。这极大地减小了自抽样得到的置信区间大小，并且在估计周期性均值抽样分布时，其统计功效和准确性优于其他方法。本文提供了对美国新冠肺炎死亡率周期性相关成分的可变带通周期块自抽样法分析，并与其他自抽样方法进行了比较。结果表明，两种方法都发现了显著的季节性成分，但可变带通周期块自抽样法产生的置信区间更小，并且只有可变带通周期块自抽样法在季节性成分的二次至五次谐波以及每周成分中发现了显著成分。这一关键证据支持了季节性模式的存在以及其他周期性相关成分的存在、它们的时间以及其影响的置信区间，这将有助于对未来新冠肺炎应对措施进行预测和准备。
