Reducción de la incertidumbre en un modelo tipo SEIR mediante Remuestreo y wavelets.
F. G. Morillas Jurado, J. Valero
En este trabajo se implementa un proceso de simulación numérica para estudiar la evolución de la primera ola de la Pandemia por COVID-19 en España en el año 2020 sobre un modelo SEIR modificado (modelo compartimental que presentes en la mayoría de procesos en Física, Biología...). Se aplican técnicas de remuestreo combinadas con graduación wavelet de manera que las estimaciones obtenidas mejoran la estimación que se realiza únicamente con los datos iniciales; reduce el error relativo en un 12%.
Las muestras bootstrap se obtienen remuestreando los residuos de las variables consideradas como inputs del modelo una vez estas han sido graduadas. La técnica de graduación utilizada está basada en la suavización de una serie numérica haciendo uso de wavelets, donde se asume que la parte de tendencia se aproxima a los valores esperados de cada variable, y la parte wavelet, correctamente tratada, está formada por las fluctuaciones aleatorias que contiene la variabilidad existente.
Keywords: SEIR, Bootstrap, wavelet,COVID-19
Scheduled
Applications of Statistics I
June 10, 2022 10:10 AM
A15
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