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
References
(1) Kowalski AM, Portesi M, Vampa V, Losada M, Holik F. Entropy-based informational study of the COVID-19 series of data. Mathematics 2022;10(23):4590.
(2) Vampa V, Kowalski AM, Losada M, Portesi M, Holik F. Information quantifiers and unpredictability in the COVID-19 time-series data. Revista de Matemática: Teoría y Aplicaciones 2023;30(1):1-23.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright (c) 2024 Victoria Vampa, Andres M. Kowalski, Federico Holik, Marcelo Losada, Mariela Portesi (Author)