Periodicity in New York State COVID-19 Hospitalizations Leveraged from the Variable Bandpass Periodic Block Bootstrap
利用可变带通周期块自助法分析纽约州新冠住院的周期性AI 解读 ↓
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利用可变带通周期块自助法分析纽约州新冠住院的周期性
- 研究问题
- 如何从纽约州新冠住院时间序列中分离年度周期相关成分，并量化其时间模式及相应统计不确定性？
- 方法与思路
- 作者采用可变带通周期块自助法，先以带通滤波隔离目标频率，再通过块重抽样保留周期相关结构。
- 材料陈述的贡献
- 摘要报告，该方法得到更窄的置信区间，并发现比较方法未检出的年度住院季节模式及其时间信息。
- 阅读关注点
- 应核对频率、带宽与块长度的选择，滤波后推断的覆盖表现，以及各比较方法是否采用可比设置。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未给出区间覆盖证据，区间更窄本身不足以证明推断更好。
原文摘要
The outbreak of the SARS-CoV-2 virus, which led to an unprecedented global pandemic, has underscored the critical importance of understanding seasonal patterns. This knowledge is fundamental for decision-making in healthcare and public health domains. Investigating the presence, intensity, and precise nature of seasonal trends, as well as these temporal patterns, is essential for forecasting future occurrences, planning interventions, and making informed decisions based on the evolution of events over time. This study employs the Variable Bandpass Periodic Block Bootstrap (VBPBB) to separate and analyze different periodic components by frequency in time series data, focusing on annually correlated (PC) principal components. Bootstrapping, a method used to estimate statistical sampling distributions through random sampling with replacement, is particularly useful in this context. Specifically, block bootstrapping, a model-independent resampling method suitable for time series data, is utilized. Its extensions are aimed at preserving the correlation structures inherent in PC processes. The VBPBB applies a bandpass filter to isolate the relevant PC frequency, thereby minimizing contamination from extraneous frequencies and noise. This approach significantly narrows the confidence intervals, enhancing the precision of estimated sampling distributions for the investigated periodic characteristics. Furthermore, we compared the outcomes of block bootstrapping for periodically correlated time series with VBPBB against those from more traditional bootstrapping methods. Our analysis shows VBPBB provides strong evidence of the existence of an annual seasonal PC pattern in hospitalization rates not detectible by other methods, providing timing and confidence intervals for their impact.