论文标题
COVID-19的光谱处理时间序列数据
Spectral Processing of COVID-19 Time-Series Data
论文作者
论文摘要
在汇总的COVID-19数据中振荡的存在不仅引发了有关数据准确性的问题，还阻碍了对大流行的理解。光谱分析用于揭示数据的其他属性，并使用正弦曲线重新合成复制振荡。还讨论了7天移动平均线的确切行为，特别是其锯齿状的原因及其引入的相位误差的原因。相比之下，其他过滤技术和傅立叶处理会产生较高的平滑性，并具有零相误差。两者都呈现，并扩展到分离几个频率范围。这提取了重新合成的一些短期变异性，并表明在美国死亡率数据中存在8到21天之间的波动。这些方法的应用程序包括建模流行病学时间序列数据，并确定数据的明显属性。
The presence of oscillations in aggregated COVID-19 data not only raises questions about the data's accuracy, it hinders understanding of the pandemic. Spectral analysis is used to reveal additional properties of the data, and the oscillations are replicated using sinusoidal resynthesis. The precise behavior of the seven-day moving average is also discussed, specifically, the cause of its jaggedness and the phase error it introduces. In comparison, other filtering techniques and Fourier processing produce superior smoothing and have zero phase error. Both of these are presented, and they are extended to isolate several frequency ranges. This extracts some of the same short-term variability that is resynthesized, and it shows that fluctuations with periods between 8 and 21 days are present in U.S. mortality data. These methods have applications that include modeling epidemiological time-series data as well as identifying less obvious properties of the data.
