Keywords
Entropía de permutaciones
Complejidad estadística
Metodología de Bandt-Pompe
Transformada Wavelet
Information theory
Permutation entropy
Statistical complexity
Bandt-Pompe methodology
Wavelet transform
How to Cite
Abstract
We apply different information quantifiers to the study of COVID-19 time series. First, we analyze how the fact of smoothing the curves alters the informational content of the series, by applying the permutation and wavelet entropies to the series of daily new cases using a sliding-window method. In addition, to study how coupled the curves associated with daily new cases of infections and deaths are, we compute the wavelet coherence. Our results show how information quantifiers can be used to analyze the unpredictable behavior of this pandemic in the short and medium terms.
References
M. B. Arouxet, A. F. Bariviera, V. E. Pastor, V. Vampa, COVID-19 impact on cryptocurrencies: evidence from a wavelet-based Hurst exponent, Physica A 596(2022), 127170. Doi: 10.1016/j.physa.2022.127170
C. Bandt, B. Pompe, Permutation entropy: a natural complexity measure for time series, Phys. Rev. Lett. 88(2002), 174102. Doi: 10.1103/PhysRevLett.88.174102
S. Blanco, A. Figliola, R. Quian Quiroga, O.A. Rosso, E. Serrano, Time-frequency analysis of electroencephalogram series. III. Wavelet packets and information cost function. Phys. Rev. E 57(1998), 932. Doi: 10.1103/PhysRevE.57.932
C. Chum, An Introduction to Wavelets, Academic Press, New York, 1992. Available from: https://www.elsevier.com/books/an-introduction-to-wavelets/chui/978-0-12-174592-9.
J. Contreras-Reyes, Fisher information and uncertainty principle for skewgaussian random variables, Fluctuation and Noise Letters 20(2021) no.5, 21500395. Doi: 10.1142/S0219477521500395
I. Daubechies, Ten Lectures on Wavelets, SIAM, 61, 1992. Doi:
1137/1.9781611970104.fm
L. Fernandes, F. Araujo, M. Silva, B. Acioli-Santos, Predictability of COVID-19 worldwide lethality using permutation-information theory quantifiers, Results in Physics 26(2021) 104306. Doi: 10.1016/j.rinp.2021.104306
L. Fernandes, F. De Araujo, J. Silva, M. Silva, Insights into the predictability and similarity of COVID-19 worldwide lethality, Fractals 29(2021), no. 07, 2150221. Doi: 10.1142/S0218348X21502212
L. Gamero, A. L. Plastino, M. E. Torres, Wavelet analysis and nonlinear dynamics in a nonextensive setting, Physica A 246(1997), 487-509. Doi: 10.1016/S0378-4371(97)00367-1
M. Henry, G. Judge, Permutation entropy & information recovery in nonlinear dynamic economic time series, Econometrics 7(2019) no. 1, 10. Doi: 10.3390/econometrics7010010
A. M. Kowalski, M. Portesi, V. Vampa, M. Losada, F. Holik, Entropy-based informational study of the COVID-19 series of data, Mathematics 10(2022), no. 23, 4590. Doi: 10.3390/math10234590
A. M. Kowalski, R. Rossignoli, E.M.F. Curado, Eds. Concepts and Recent Advances in Generalized Information Measures and Statistics, Bentham SciencePublishers 2013. Doi: 10.2174/97816080576031130101
M. Kumar, R. Pachori, U. Acharya, Automated diagnosis of myocardial infarction ECG signals using sample entropy in flexible analytic wavelet transform framework, Entropy 19(2017), no. 9, 488. Doi: 10.3390/e19090488
X. Li, G. Ouyang, D. Richards, Predictability analysis of absence seizures with permutation entropy, Epilepsy Research 77(2007), 70-74. Doi: 10.1016/j.eplepsyres.2007.08.002
D. Meintrup, M. Nowak-Machen, S. Borgmann, Nine Months of COVID-19 Pandemic in Europe: A Comparative Time Series Analysis of Cases and Fatalities in 35 Countries, International Journal of Environmental Research and Public Health 18(2021), no. 12, 6680.
Doi: 10.3390/ijerph18126680
F. Mitroi-Symeonidis, I. Anghel, O. Lalu, C. Popa, The Permutation Entropy and its Applications on Fire Tests Data, J. Appl. Comput. Mech. 6(2020), no. SI, 1380-1393.
Doi: 10.22055/jacm.2020.34707.2464f
F. Mitroi-Symeonidis, I. Anghel, A. Tozzi, Preventing a COVID-19 pandemic flashover (electronic response to: Day M. 2020. Covid-19: identifying and isolating asymptomatic people helped eliminate virus in Italian village), 2020. Doi: 10.13140/RG.2.2.34525.97768
O. Nicolis, J. Mateu, J. Contreras-Reyes, Wavelet-Based Entropy Measures to Characterize Two-Dimensional Fractional Brownian Fields, Entropy 22(2020), no. 2, 196. Doi: 10.3390/e22020196
G. Ouyang, Permutation entropy, 2021. Available at: https://www.mathworks.com/matlabcentral/fileexchange/37289-permu
tation-entropy. Retrieved June 23, 2021.
F. Olivares, A. L. Plastino, O. A. Rosso, Ambiguities in Bandt-Pompe’s methodology for local entropic quantifiers, Physica A 391(2012), 2518-2526. Doi: 10.1016/j.physa.2011.12.033
G. Ouyang, J. Li, X. Liu, X. Li, Dynamic characteristics of absence EEG recordings with multiscale permutation entropy analysis, Epilepsy Research 104(2013), no. 3, 246-252.
Doi: j.eplepsyres.2012.11.003
H. Ritchie, E. Ortiz-Ospina, D. Beltekian, E. Mathieu, J Hasell, B. Macdonald, C. Giattino, C. Appel, L. Rodes-Guirao, M. Roser, Coronavirus Pandemic (COVID-19), 2020. Available at https://www.ourworldindata.org/coronavirus
O. A. Rosso, H. Larrondo, M. T. Martin, A. L. Plastino, M. Fuentes, Distinguishing Noise from Chaos, Phys. Rev. Lett. 99(2007) 154102. Doi: 10.1103/PhysRevLett.99.154102
O. A. Rosso, L. De Micco, H. Larrondo, M. Martin, A. L. Plastino, Generalized statistical complexity measure, Int. J. Bif. and Chaos 20(2010), 775-785. Doi: 10.1142/S021812741002606X
O. A. Rosso, L. De Micco, A. L. Plastino, H. Larrondo, Info-quantifiers’ map-characterization revisited. Physica A 389(2010), 4604-4612. Doi: 10.1016/j.physa.2010.06.055
V. Solovieva, A. Bielinskyia, N. Kharadzjana. Coverage of the coronavirus pandemic through entropy measures, in: CS & SE SW 2020: 3rd Workshop for Young Scientists in Computer Science & Software Engineering, Kryvyi Rih, Ukraine, 2020, 24-42. Available at: https://ceur-ws.org/Vol-2832/paper02.pdf
C. Torrence, G. Compo. A, Practical Guide toWavelet Analysis, Bulletin of the American Meteorological Society, 79(1998): no. 1. Doi: 10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2
E. Valverde, G. Clemente, P. Arini, V. Vampa, Wavelet-based entropy and complexity to identify cardiac electrical instability in patients post myocardial infarction, Biomedical Signal Processing and Control 69(2021), 102846. Doi: 10.1016/j.bspc.2021.102846
M. Zanin, L. Zunino, O. A. Rosso, D. Papo, Permutation entropy and its main biomedical and econophysics applications: a review, Entropy 14(2012), no. 8, 1553-1577. Doi:10.3390/e14081553
S. Zozor, M. Portesi, P. W. Lamberti, G. M. Bosyk, J. F. Bercher (Eds), Entropies, Divergences, Information, Identities and Inequalities, Entropy Special Issue (2021). Available at https://www.mdpi.com/journal/entropy/special_issues/entro_inqua
Comments
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright (c) 2023 Victoria Vampa, Andrés M. Kowalski, Marcelo Losada, Mariela Portesi, Federico Holik