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Entropy-Based Informational Study of the COVID-19 Series of Data

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  • Andres M. Kowalski

    (Instituto de Física La Plata (IFLP), CONICET, Diag. 113 e/63 y 64, 1900 La Plata, Argentina
    Facultad de Ciencias Exactas, Universidad Nacional de La Plata, C.C. 67, 1900 La Plata, Argentina)

  • Mariela Portesi

    (Instituto de Física La Plata (IFLP), CONICET, Diag. 113 e/63 y 64, 1900 La Plata, Argentina
    Facultad de Ciencias Exactas, Universidad Nacional de La Plata, C.C. 67, 1900 La Plata, Argentina)

  • Victoria Vampa

    (Uidet Matemática Aplicada, Facultad de Ingeniería, Universidad Nacional de La Plata, Avda. 1 y 47, 1900 La Plata, Argentina)

  • Marcelo Losada

    (Facultad de Matemática, Astronomía, Física y Computación, Universidad Nacional de Córdoba, Av. Medina Allende, X5000HUA Córdoba, Argentina)

  • Federico Holik

    (Instituto de Física La Plata (IFLP), CONICET, Diag. 113 e/63 y 64, 1900 La Plata, Argentina)

Abstract

Since the appearance in China of the first cases, the entire world has been deeply affected by the flagellum of the Coronavirus Disease (COVID-19) pandemic. There have been many mathematical approaches trying to characterize the data collected about this serious issue. One of the most important aspects for attacking a problem is knowing what information is really available. We investigate here the information contained in the COVID-19 data of infected and deceased people in all countries, using informational quantifiers such as entropy and statistical complexity. For the evaluation of these quantities, we use the Bandt–Pompe permutation methodology, as well as the wavelet transform, to obtain the corresponding probability distributions from the available series of data. The period analyzed covers from the appearance of the disease up to the massive use of anti-COVID vaccines.

Suggested Citation

  • Andres M. Kowalski & Mariela Portesi & Victoria Vampa & Marcelo Losada & Federico Holik, 2022. "Entropy-Based Informational Study of the COVID-19 Series of Data," Mathematics, MDPI, vol. 10(23), pages 1-16, December.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:23:p:4590-:d:992961
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    References listed on IDEAS

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