A Bayesian Model of COVID-19 Cases Based on the Gompertz Curve

Ángel Berihuete, Marta Sánchez-Sánchez and Alfonso Suárez-Llorens
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Ángel Berihuete: Dpto. Estadística e Investigación Operativa, Universidad de Cádiz, 11510 Puerto Real, Spain
Marta Sánchez-Sánchez: IBiDat UC3M-Santander Big Data Institute, Universidad Carlos III de Madrid, 28903 Getafe, Madrid, Spain
Alfonso Suárez-Llorens: Dpto. Estadística e Investigación Operativa, Universidad de Cádiz, 11510 Puerto Real, Spain

Mathematics, 2021, vol. 9, issue 3, 1-16

Abstract: The COVID-19 pandemic has highlighted the need for finding mathematical models to forecast the evolution of the contagious disease and evaluate the success of particular policies in reducing infections. In this work, we perform Bayesian inference for a non-homogeneous Poisson process with an intensity function based on the Gompertz curve. We discuss the prior distribution of the parameter and we generate samples from the posterior distribution by using Markov Chain Monte Carlo (MCMC) methods. Finally, we illustrate our method analyzing real data associated with COVID-19 in a specific region located at the south of Spain.

Keywords: Bayesian inference; modeling epidemics; non-homogeneous poisson process; Gompertz curve; inverse Gaussian (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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