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

Andres M. Kowalski (), Mariela Portesi, Victoria Vampa, Marcelo Losada and Federico Holik
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Andres M. Kowalski: Instituto de Física La Plata (IFLP), CONICET, Diag. 113 e/63 y 64, 1900 La Plata, Argentina
Mariela Portesi: Instituto de Física La Plata (IFLP), CONICET, Diag. 113 e/63 y 64, 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

Mathematics, 2022, vol. 10, issue 23, 1-16

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.

Keywords: information theory; permutation entropy; statistical complexity; Bandt–Pompe methodology; wavelet transform (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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