■COVID-19大流行见证了广泛的感染和变异。特别是,东京面临着七波COVID-19的挑战,在此期间,政府干预发挥了关键作用。因此,全面了解政府控制措施至关重要,这有利于卫生当局在政策制定过程中。
■我们的研究分析了2020年1月16日至2022年9月30日东京每日COVID-19感染计数的每日变化数据。我们利用自适应傅里叶分解(AFD)分析COVID-19数据中的时间趋势。它扩展了传统的AFD方法,通过在不同的时间频率范围内基于多个单独的组件构建新的组件。此外,我们对一阶到三阶合成结果进行了皮尔逊相关性评估,以及与其他信号分析技术的比较分析。最终,这些新组件与跨不同时间段的政策数据集成在一起,以便进行全面分析。
■使用AFD对东京每日COVID-19数据的分析揭示了各种政府政策如何影响七个不同波动时期的感染率。在分解结果中,减少营业时间政策与前四波中的高频成分相关,而第六波的低频分量表明其相关性下降。疫苗接种政策最初显示出与第五波的中频相关性,并在最后一个波中以低频相关性继续。此外,我们的统计分析(值p<0.05)表明,75%的三阶AFD成分与原始感染呈显著正相关,而EMD和VMD中大多数成分的相关系数没有达到显著性。
■在时频域中,与EMD和VMD相比,AFD在捕获与流行病控制措施相关的关键数据方面表现出卓越的性能。在各种政策下,这七个时期的每日COVID-19感染计数的变化在不同的三阶AFD组件中很明显。这些发现指导了未来公共卫生政策和社会措施的制定。
The COVID-19 pandemic has witnessed widespread infections and variants. Particularly,
Tokyo faced the challenge of seven waves of COVID-19, during which government interventions played a pivotal role. Therefore, gaining a comprehensive understanding of government control measures is of paramount importance, which is beneficial for health authorities in the policy development process.
Our study analysis the daily change data of the daily COVID-19 infection count in
Tokyo from January 16, 2020 to September 30, 2022. We utilized adaptive Fourier decomposition (AFD) for analyzing the temporal trends within COVID-19 data. It extends the conventional AFD approach by constructing new components base on multiple individual components at various time-frequency scales. Furthermore, we conducted Pearson correlation assessments of the first to third-order synthesis results, along with comparative analyses against other signal analysis techniques. Ultimately, these new components are integrated with policy data spanning different time periods for a comprehensive analysis.
The analysis of daily COVID-19 data in
Tokyo using AFD reveals how various government policies impacted infection rates across seven distinct fluctuation periods. In the decomposition results, the reduction of business hours policy correlated with high-frequency components in the first four waves, while the low-frequency components for the sixth wave suggested a decline in its relevance. The vaccination policy initially displayed a mid-frequency correlation with the fifth wave and continued with a low-frequency correlation in the last wave. Moreover, our statistical analysis (value of p < 0.05) demonstrated that 75% of the third-order AFD components exhibited significant positive correlations with the original infections, while the correlation coefficients of most components in EMD and VMD did not attain significance.
In the time-frequency domain, AFD demonstrates superior performance compared to EMD and VMD in capturing crucial data related to epidemic control measures. The variations in daily COVID-19 infection counts during these seven periods under various policies are evident in distinct third-order AFD components. These findings guide the formulation of future public health policies and social measures.