@misc{10498/24604,
year = {2021},
month = {2},
url = {http://hdl.handle.net/10498/24604},
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.},
organization = {Ministerio de Economía y Competitividad. Gobierno de España; 
2014-2020 ERDF Operational Programme;  Consejería de Economía, Conocimiento, Empresas y Universidad.Junta de Andalucía},
publisher = {MDPI},
keywords = {Bayesian inference},
keywords = {modeling epidemics},
keywords = {non-homogeneous poisson process},
keywords = {Gompertz curve},
keywords = {inverse Gaussian},
title = {A Bayesian Model of COVID-19 Cases Based on the Gompertz Curve},
doi = {10.3390/math9030228},
author = {Berihuete Macías, Ángel and Sánchez Sánchez, Marta and Suárez Llorens, Alfonso},
}