Adaptive Fourier Decomposition of the First Three SARS-CoV-2 Infection Waves with Epidemic Intervention — London, UK, 2020–2022
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- TL;DR
- This study analyzes daily COVID-19 case fluctuations in London from January 2020 to February 2022 to understand how government responses and viral mutations affected case numbers.
- Problem
- Understanding the complex interactions among government pandemic responses, viral mutations, and changes in COVID-19 case incidences requires detailed analysis of daily infection fluctuations over time.
- Method
- Researchers used the adaptive Fourier decomposition (AFD) method to analyze diurnal changes, segmenting the AFD into multi-component groups evaluated with Pearson correlation and compared against other signal analysis techniques.
- Results
- The analysis revealed a strong correlation between the "stay-at-home" directive and high-frequency components during the first epidemic wave, highlighting the importance of sustained policy implementation.
- Contributions
- The study introduced a novel approach using basis decomposition methods to differentiate individual components across various time-frequency scales.
- Limitations
- Not specified in the abstract.
- Takeaways
- The AFD component method offers a comprehensive tool for analyzing the immediate and prolonged impacts of public health policies, providing insights to guide future preventive strategies.
- Applications
- Not specified in the abstract.
- Topics
- Adaptive Fourier Decomposition; COVID-19 Epidemic Waves; Epidemic Intervention Analysis
- For industry
- Healthcare and public health
- Why it matters
- Provides robust analytical tools for evaluating public health interventions and pandemic data.
Abstract
Background: This study provides a detailed analysis of the daily fluctuations in coronavirus disease 2019 (COVID-19) case numbers in London from January 31, 2020 to February 24, 2022. The primary objective was to enhance understanding of the interactions among government pandemic responses, viral mutations, and the subsequent changes in COVID-19 case incidences. Methods: We employed the adaptive Fourier decomposition (AFD) method to analyze diurnal changes and further segmented the AFD into novel multi-component groups consisting of one to three elements. These restructured components were rigorously evaluated using Pearson correlation, and their effectiveness was compared with other signal analysis techniques. This study introduced a novel approach to differentiate individual components across various time-frequency scales using basis decomposition methods. Results: Analysis of London's daily COVID-19 data using AFD revealed a strong correlation between the "stay at home" directive and high-frequency components during the first epidemic wave. This indicates the need for sustained implementation of vaccination policies to maintain their effectiveness. Discussion: The AFD component method provides a comprehensive analysis of the immediate and prolonged impact of governmental policies on the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This robust tool has proven invaluable for analyzing COVID-19 pandemic data, offering critical insights that guide the formulation of future preventive and public health strategies.