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The use of the dynamic time warping (DTW) method to describe the COVID-19 dynamics in Poland

Author

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  • Joanna Landmesser

    (Warsaw University of Life Sciences – SGGW, Poland)

Abstract

Research background: In recent times, the whole world has been severely affected by the COVID-19 pandemic. The influence of the epidemic on the society and the economy has caused a great deal of scientific interest. The development of the pandemic in many countries was analyzed using various models. However, the literature on the dissemination of COVID-19 lacks econometric analyzes of the development of this epidemic in Polish voivodeships. Purpose of the article: The aim of the study is to find similarities in time series for infected with and those who died of COVID-19 in Polish voivodeships using the method of dynamic time warping. Methods: The dynamic time warping method allows to calculate the distance between two time series of different lengths. This feature of the method is very important in our analysis because the coronavirus epidemic did not start in all voivodeships at the same time. The dynamic time warping also enables an adjustment of the timeline to find similar, but shifted, phases. Using this method, we jointly analyze the number of infected and deceased people in each province. In the next step, based on the measured similarity of the time series, the voivodeships are grouped hierarchically. Findings & value added: We use the dynamic time warping to identify groups of voivodeships affected by the epidemic to a different extent. The classification performed may be useful as it indicates patterns of the COVID-19 disease evolution in Polish voivodeships. The results obtained at the regional level will allow better prediction of future infections. Decision makers should formulate further recommendations for lockdowns at the local level, and in the long run, adjust the medical infrastructure in the regions accordingly. Policymakers in other countries can benefit from the findings by shaping their own regional policies accordingly.

Suggested Citation

  • Joanna Landmesser, 2021. "The use of the dynamic time warping (DTW) method to describe the COVID-19 dynamics in Poland," Oeconomia Copernicana, Institute of Economic Research, vol. 12(3), pages 539-556, September.
    • Handle: RePEc:pes:ieroec:v:12:y:2021:i:3:p:539-556
    DOI: 10.24136/oc.2021.018
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    References listed on IDEAS

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    4. Katarzyna Czech & Michał Wielechowski & Pavel Kotyza & Irena Benešová & Adriana Laputková, 2020. "Shaking Stability: COVID-19 Impact on the Visegrad Group Countries’ Financial Markets," Sustainability, MDPI, vol. 12(15), pages 1-19, August.
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    6. Pedro Pardal & Rui Dias & Petr Suler & Nuno Teixeira & Tomas Krulicky, 2020. "Integration in Central European capital markets in the context of the global COVID-19 pandemic," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 15(4), pages 627-650, December.
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    Cited by:

    1. Beata Bieszk-Stolorz & Krzysztof Dmytrów, 2022. "Assessment of the Similarity of the Situation in the EU Labour Markets and Their Changes in the Face of the COVID-19 Pandemic," Sustainability, MDPI, vol. 14(6), pages 1-20, March.
    2. Beata Bieszk-Stolorz & Iwona Markowicz, 2022. "The Impact of the COVID-19 Pandemic on the Situation of the Unemployed in Poland. A Study Using Survival Analysis Methods," Sustainability, MDPI, vol. 14(19), pages 1-19, October.
    3. Krzysztof Dmytrów & Beata Bieszk-Stolorz & Joanna Landmesser-Rusek, 2022. "Sustainable Energy in European Countries: Analysis of Sustainable Development Goal 7 Using the Dynamic Time Warping Method," Energies, MDPI, vol. 15(20), pages 1-17, October.
    4. Krzysztof Dmytrow & Beata Bieszk-Stolorz, 2021. "Comparison of changes in the labour markets of post-communist countries with other EU member states," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, vol. 16(4), pages 741-764, December.
    5. Lolea Iulian Cornel & Stamule Simona, 2021. "Trading using Hidden Markov Models during COVID-19 turbulences," Management & Marketing, Sciendo, vol. 16(4), pages 334-351, December.

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    More about this item

    Keywords

    COVID-19; coronavirus; regional analysis; dynamic time warping; clustering;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • I15 - Health, Education, and Welfare - - Health - - - Health and Economic Development

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