Information quantifiers and unpredictability in the COVID-19 time-series data

• Vampa, Victoria ^[1] ; Kowalski, Andrés M. ^[2] ; Losada, Marcelo ^[3] ; Portesi, Mariela ^[2] ; Holik, Federico ^[2]
  1. [1] Universidad Nacional de La Plata

     Universidad Nacional de La Plata

     Argentina

     
  2. [2] Instituto de Física La Plata

     Instituto de Física La Plata

     Argentina

     
  3. [3] Universidad Nacional de Córdoba

     Universidad Nacional de Córdoba

     Argentina

     

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• Localización: Revista de Matemática: Teoría y Aplicaciones, ISSN 2215-3373, ISSN-e 2215-3373, Vol. 30, Nº. 1, 2023 (Ejemplar dedicado a: Revista de Matemática: Teoría y Aplicaciones), págs. 1-23
• Idioma: inglés
• DOI: 10.15517/rmta.v30i1.50554
• Títulos paralelos:
  • Cuantificadores de información e impredictibilidad en las series temporales asociadas a la COVID-19
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    Aplicamos diferentes cuantificadores de informacion al estudio de series temporales de COVID-19. En primer lugar, analizamos como el hecho de suavizar las curvas altera el contenido de informacion de la serie, aplicando la entropia de permutaciones y la entropia wavelet a la serie de casos diarios nuevos mediante un metodo de ventana movil. Ademas, para estudiar que tan acopladas estan las curvas asociadas con los nuevos casos diarios de infecciones y muertes, calculamos la coherencia wavelet. Nuestros resultados muestran como se pueden utilizar cuantificadores de información para analizar el comportamiento impredecible de esta pandemia en el corto y mediano plazo.

  • English

    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.

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