Adaptive Fourier Decomposition of the First Three SARS-CoV-2 Infection Waves with Epidemic Intervention—London,UK,2020-2022  

作　　者：Zige Liu Guibin Lu Cheokieng Vong Zhiqi Zeng Wei He Zhijie Lin Cuiyun Lin Kaichin Hsieh Zifeng Yang Arlindo L.Oliveira Chitin Hon 

机构地区：[1]Department of Engineering Science,Faculty of Innovation Engineering,Macao University of Science and Technology,Macao SAR,China [2]Queen Ethelburga’s Collegiate,Thorpe Underwood Estate,York,UK [3]State Key Laboratory of Respiratory Disease,National Clinical Research Center for Respiratory Disease,Guangzhou Institute of Respiratory Health,The First Affiliated Hospital of Guangzhou Medical University,Guangzhou City,Guangdong Province,China [4]Respiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications,Department of Engineering Science,Faculty of Innovation Engineering,Macao University of Science and Technology,Macao SAR,China [5]Guangzhou Key Laboratory for Clinical Rapid Diagnosis and Early Warning of Infectious Diseases,KingMed School of Laboratory Medicine,Guangzhou Medical University,Guangzhou City,Guangdong Province,China [6]Faculty of Innovation Engineering,School of Computer Science and Engineering,Macao University of Science and Technology,Macao SAR,China [7]University College London,UCL Faculty of Engineering Sciences,London,UK [8]Guangzhou Laboratory,Guangzhou City,Guangdong Province,China [9]Instituto de Engenharia de Sistemas e Computadores:Investigação e Desenvolvimento em Lisboa,Lisboa,Portugal [10]Instituto Superior Técnico,Universidade de Lisboa,Lisboa,Portugal

出　　处：《China CDC weekly》2024年第21期478-486,I0006-I0008,共12页中国疾病预防控制中心周报（英文）

基　　金：Supported by the Science and Technology Program of Guangzhou(2022B01W0003);the National Basic Research Priorities Program of China(2023YFC3041600,2023YFC3041800);the Science and Technology Development Fund of Macao SAR(005/2022/ALC);the self-supporting Program of Guangzhou Laboratory(SRPG22-007).

摘　　要：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.

关 键 词：offering SUSTAINED LONDON 

分 类 号：R563.1[医药卫生—呼吸系统] R18[医药卫生—内科学]