Evidence map›Paper›PMID 38854463›Full record

ArticleChina CDC weekly2024

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

Zige Liu, Guibin Lu, Cheokieng Vong, Zhiqi Zeng, Wei He, Zhijie Lin, Cuiyun Lin, Kaichin Hsieh, Zifeng Yang, Arlindo L Oliveira and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in China CDC weekly, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Zige LiuDepartment of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau SAR, China.
Guibin LuDepartment of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau SAR, China.
Cheokieng VongQueen Ethelburga's Collegiate, Thorpe Underwood Estate, York, UK.
Zhiqi ZengState 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.
Wei HeDepartment of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau SAR, China.
Zhijie LinDepartment of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau SAR, China.
Cuiyun LinFaculty of Innovation Engineering, School of Computer Science and Engineering, Macau University of Science and Technology, Macau SAR, China.
Kaichin HsiehUniversity College London, UCL Faculty of Engineering Sciences, London, UK.
Zifeng YangState 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.
Arlindo L OliveiraRespiratory Disease AI Laboratory on Epidemic and Medical Big Data Instrument Applications, Department of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau SAR, China.
Chitin HonDepartment of Engineering Science, Faculty of Innovation Engineering, Macau University of Science and Technology, Macau SAR, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Adaptive Fourier DecompositionCOVID-19Daily FluctuationsGovernmental Preventive Policy

Identifiers

PMID38854463
PMCPMC11154110

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-SA
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.