IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0260899.html
   My bibliography  Save this article

Application of principal component analysis on temporal evolution of COVID-19

Author

Listed:
  • Ashadun Nobi
  • Kamrul Hasan Tuhin
  • Jae Woo Lee

Abstract

The COVID-19 is one of the worst pandemics in modern history. We applied principal component analysis (PCA) to the daily time series of the COVID-19 death cases and confirmed cases for the top 25 countries from April of 2020 to February of 2021. We calculated the eigenvalues and eigenvectors of the cross-correlation matrix of the changes in daily accumulated data over monthly time windows. The largest eigenvalue describes the overall evolution dynamics of the COVID-19 and indicates that evolution was faster in April of 2020 than in any other period. By using the first two PC coefficients, we can identify the group dynamics of the COVID-19 evolution. We observed groups under critical states in the loading plot and found that American and European countries are represented by strong clusters in the loading plot. The first PC plays an important role and the correlations (C1) between the normalized logarithmic changes in deaths or confirmed cases and the first PCs may be used as indicators of different phases of the COVID-19. By varying C1 over time, we identified different phases of the COVID-19 in the analyzed countries over the target time period.

Suggested Citation

  • Ashadun Nobi & Kamrul Hasan Tuhin & Jae Woo Lee, 2021. "Application of principal component analysis on temporal evolution of COVID-19," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-12, December.
    • Handle: RePEc:plo:pone00:0260899
    DOI: 10.1371/journal.pone.0260899
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0260899
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0260899&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0260899?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Nobi, Ashadun & Alam, Shafiqul & Lee, Jae Woo, 2017. "Dynamic of consumer groups and response of commodity markets by principal component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 337-344.
    2. Nobi, Ashadun & Lee, Jae Woo, 2016. "State and group dynamics of world stock market by principal component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 450(C), pages 85-94.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Luu, Duc Thi & Yanovski, Boyan & Lux, Thomas, 2018. "An analysis of systematic risk in worldwide econonomic sentiment indices," Economics Working Papers 2018-03, Christian-Albrechts-University of Kiel, Department of Economics.
    2. Nobi, Ashadun & Alam, Shafiqul & Lee, Jae Woo, 2017. "Dynamic of consumer groups and response of commodity markets by principal component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 337-344.
    3. Jae Woo Lee & Ashadun Nobi, 2018. "State and Network Structures of Stock Markets around the Global Financial Crisis," Papers 1806.04363, arXiv.org.
    4. Valentin Burca, 2021. "Portfolio Optimization Using an Alternative Approach. Towards Data Mining Techniques Way," Economic Alternatives, University of National and World Economy, Sofia, Bulgaria, issue 1, pages 113-133, March.
    5. Jae Woo Lee & Ashadun Nobi, 2018. "State and Network Structures of Stock Markets Around the Global Financial Crisis," Computational Economics, Springer;Society for Computational Economics, vol. 51(2), pages 195-210, February.
    6. Lei Wang & Yan Yan & Xiaoteng Li & Xiaosong Chen, 2018. "General Component Analysis (GCA): A new approach to identify Chinese corporate bond market structures," PLOS ONE, Public Library of Science, vol. 13(7), pages 1-18, July.
    7. Thomas Lux & Duc Thi Luu & Boyan Yanovski, 2020. "An analysis of systemic risk in worldwide economic sentiment indices," Empirica, Springer;Austrian Institute for Economic Research;Austrian Economic Association, vol. 47(4), pages 909-928, November.
    8. Bui Thanh Khoa & Tran Trong Huynh & Vo Dinh Nhat Truong & Le Vu Truong & Do Bui Xuan Cuong & Tran Khanh, 2023. "Minimal Spanning Tree application to determine market correlation structure," HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE - ENGINEERING AND TECHNOLOGY, HO CHI MINH CITY OPEN UNIVERSITY JOURNAL OF SCIENCE, HO CHI MINH CITY OPEN UNIVERSITY, vol. 13(1), pages 64-71.
    9. Teng, Sin Yong & How, Bing Shen & Leong, Wei Dong & Teoh, Jun Hao & Cheah, Adrian Chee Siang & Motavasel, Zahra & Lam, Hon Loong, 2019. "Principal component analysis-aided statistical process optimisation (PASPO) for process improvement in industrial refineries," MPRA Paper 94058, University Library of Munich, Germany, revised 01 Jan 2019.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0260899. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.