M. Sánchez Sánchez, A. Suárez Llorens, Á. Berihuete

Dada la necesidad de encontrar modelos matemáticos para pronosticar la evolución de la pandemia de COVID-19, en este trabajo utilizaremos la inferencia bayesiana en un proceso de Poisson no homogéneo con una función de intensidad basada en la curva de Gompertz. Utilizando este modelo de Gompertz junto con los conocimientos previos disponibles sobre el coronavirus SARS-COV-2, obtendremos una distribución previa de los parámetros subyacentes. Después, obtendremos la distribución a posteriori utilizando métodos MCMC para pronosticar el número de nuevos casos de COVID-19 en intervalos de tiempo en un futuro cercano desde una perspectiva bayesiana. Finalmente, utilizando la información oficial facilitada por el gobierno de España, mostraremos un ejemplo particular de previsiones en diferentes momentos de la pandemia.

Keywords: Inferencia Bayesiana, curva de Gompertz, COVID-19, Proceso de Poisson no homogeneo, distribución a priori.

Scheduled

Bayesian methods
June 7, 2022  4:50 PM
Audiovisual room


Other papers in the same session

Approximate Bayesian Inference with Implicit Stochastic Processes

S. Rodríguez Santana, B. Zaldívar, D. Hernández Lobato

Nuevas estrategias para estimar los parámetros de la GPD

M. Martínez Pizarro, J. Martín Jiménez, E. López Sanjuán, M. I. Parra Arévalo


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