Zhang Xi, Shi Weikai, Liu Zige, Finley Aaron, Cen Kerui, Xie Zhiyao, Yang Pengjie, Li Huitao, Leong Unian, Zhang Xiaoping, Kwong Honchon, Li Matthew Tsunlung, Lu Guibin
Faculty of Chinese Medicine, Macau University of Science and Technology, Macau, China.
Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China.
J Thorac Dis. 2025 Jun 30;17(6):3516-3531. doi: 10.21037/jtd-2024-2141. Epub 2025 Jun 9.
There is a lack of a method supported by mathematical theory to compare infection rate fluctuations with the timing of policy implementation and determine the degree of correlation in South Korean cities. We evaluated the impact of the adaptive Fourier decomposition (AFD) method on the time trends and policies of coronavirus disease 2019 (COVID-19) data in South Korea, aiming to study whether AFD can contribute to future infectious disease prevention and control strategies.
This study utilized AFD to analyze temporal trends in COVID-19 data, demonstrating an effective method to illustrate the dynamics of the outbreak and comprehend the real-time impact of Korean policies on COVID-19 transmission. AFD is a novel signal decomposition algorithm that characterizes analytic signals through a linear combination of adaptive basis functions. In each AFD decomposition step, basic functions are selected from an overcomplete dictionary, and the process continues until the energy difference between the original and reconstructed signals fall below a predefined tolerance.
We found the reconstructed components of AFD are integrated with policy data spanning various periods, enabling a comprehensive analysis. The decomposition results indicated that the reduced business hours policy was closely associated with the high-frequency components observed in the first two waves. Vaccination policies were initially associated with the fourth wave. In addition, our statistical analyses showed that the third-order components of AFD were significantly and positively correlated with the original infections. In contrast, the correlation coefficients of most components in empirical modal decomposition (EMD) and variational modal decomposition (VMD) did not reach significance.
AFD is a novel algorithm. We found that AFD was able to assess the real-time impact of South Korean policies on the spread of COVID-19 by analyzing data from different stages of COVID-19 in South Korea. We infer that this method can guide future epidemic prevention and control strategies.
在韩国城市中，缺乏一种得到数学理论支持的方法来比较感染率波动与政策实施时间，并确定两者的相关程度。我们评估了自适应傅里叶分解（AFD）方法对韩国2019冠状病毒病（COVID-19）数据的时间趋势和政策的影响，旨在研究AFD是否有助于未来的传染病预防和控制策略。
本研究利用AFD分析COVID-19数据的时间趋势，证明了一种说明疫情动态并理解韩国政策对COVID-19传播实时影响的有效方法。AFD是一种新颖的信号分解算法，通过自适应基函数的线性组合来表征解析信号。在每个AFD分解步骤中，从一个超完备字典中选择基本函数，该过程持续进行，直到原始信号和重构信号之间的能量差降至预定义的容差以下。
我们发现AFD的重构分量与跨越不同时期的政策数据相结合，从而能够进行全面分析。分解结果表明，缩短营业时间政策与前两波中观察到的高频分量密切相关。疫苗接种政策最初与第四波相关。此外，我们的统计分析表明，AFD的三阶分量与原始感染显著正相关。相比之下，经验模态分解（EMD）和变分模态分解（VMD）中大多数分量的相关系数未达到显著水平。
AFD是一种新颖的算法。我们发现，通过分析韩国COVID-19不同阶段的数据，AFD能够评估韩国政策对COVID-19传播的实时影响。我们推断该方法可以指导未来的疫情防控策略。
