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Conference Abstract

Information quantifiers and wavelet coherence in time-series associated to COVID-19

By
Victoria Vampa ,
Victoria Vampa

UNLP, Facultad de Ingeniería, Departamento de Ciencias Básicas. La Plata, Argentina

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Andres M. Kowalski ,
Andres M. Kowalski

CICPBA & IFLP (CONICET-UNLP). La Plata, Argentina

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Federico Holik ,
Federico Holik

IFLP (CONICET-UNLP). La Plata, Argentina

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Marcelo Losada ,
Marcelo Losada

UNC, Facultad de Matemática, Astronomía, Física y Computación. Córdoba, Argentina

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Mariela Portesi ,
Mariela Portesi

IFLP (CONICET-UNLP). La Plata, Argentina

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Abstract

In the present investigation diverse information quantifiers have been applied to the study of time-series of COVID-19. First, it has been analyzed how the smoothing of the curves affects the informative content of the series, using permutation and wavelet entropies for the series of new daily cases, by means of a sliding-windows’ method. Besides, in order to evaluate the relationship between the curves of new daily cases of infections and deaths, the wavelet coherence has been calculated. The results show the utility of information quantifiers to understand the unpredictable behaviour of the pandemics in the short and mean time

How to Cite

1.
Vampa V, Kowalski AM, Holik F, Losada M, Portesi M. Information quantifiers and wavelet coherence in time-series associated to COVID-19. SCT Proceedings in Interdisciplinary Insights and Innovations [Internet]. 2024 May 8 [cited 2024 Jun. 16];2:303. Available from: https://proceedings.saludcyt.ar/index.php/piii/article/view/303

The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.

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