Adaptive Fourier decomposition analysis of different pandemic stages in South Korean cities: policies and trends.

BACKGROUND

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.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.