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Clustering of time series for the analysis of the COVID-19 pandemic evolution

Author

Listed:
  • Juan Gabriel Brida

    (Universidad de la República (Montevideo, Uruguay))

  • Emiliano Alvarez

    (Universidad de la República (Montevideo, Uruguay))

  • Erick Limas

    (Freie Universität Berlin)

Abstract

This study explores the dynamics of the COVID-19 pandemic by comparing the time series of ac-tive cases per population of 191 countries. Data from “Our World in Data†are examined, and Min-imal Spanning Trees and a Hierarchical Trees are used to detect groups of countries that share simi-lar performance on dynamics of coronavirus spread. Three main clusters are detected (with 104, 43 and 43 countries) and a small group composed by Mongolia and the average of all the world. The most numerous group has not reached its maximum yet and maintains a growing trend, group 2 was the first to reach the peak of daily infections and quickly entered into a phase of decline, whereas group 3 had an abrupt increase in new cases between days 20 and 40 and then entered into a de-creasing phase. The differences between the dynamics can be explained by the actions taken: there is an association between better performance and implementation of more stringent measures, as well with the realization of a greater number of tests. The results are used to discuss the dichotomy between the economic performance and health, showing that restriction policies are associated with a decrease in the number of infections. This comparative study can serve to identify the optimal public policies to minimize the number of cases and the death rate of COVID-19 in a country.

Suggested Citation

  • Juan Gabriel Brida & Emiliano Alvarez & Erick Limas, 2021. "Clustering of time series for the analysis of the COVID-19 pandemic evolution," Economics Bulletin, AccessEcon, vol. 41(3), pages 1082-1096.
    • Handle: RePEc:ebl:ecbull:eb-20-00907
    as

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    References listed on IDEAS

    as
    1. Zhixian Lin & Christopher M. Meissner, 2020. "Health vs. Wealth? Public Health Policies and the Economy During Covid-19," NBER Working Papers 27099, National Bureau of Economic Research, Inc.
    2. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    3. Jaroslaw Kwapien & Sylwia Gworek & Stanislaw Drozdz & Andrzej Gorski, 2009. "Analysis of a network structure of the foreign currency exchange market," Papers 0906.0480, arXiv.org.
    4. Goutte, Stéphane & Péran, Thomas & Porcher, Thomas, 2020. "The role of economic structural factors in determining pandemic mortality rates: Evidence from the COVID-19 outbreak in France," Research in International Business and Finance, Elsevier, vol. 54(C).
    5. Jarosław Kwapień & Sylwia Gworek & Stanisław Drożdż & Andrzej Górski, 2009. "Analysis of a network structure of the foreign currency exchange market," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 4(1), pages 55-72, June.
    6. Robert J. Hill, 1999. "International Comparisons Using Spanning Trees," NBER Chapters, in: International and Interarea Comparisons of Income, Output, and Prices, pages 109-120, National Bureau of Economic Research, Inc.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    COVID-19; Correlation Distance; Hierarchical Clustering; Minimal Spanning Trees; Hierarchical Trees;
    All these keywords.

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • I1 - Health, Education, and Welfare - - Health

    Statistics

    Access and download statistics

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