RT journal article
T1 A Bayesian Model of COVID-19 Cases Based on the Gompertz Curve
A1 Berihuete Macías, Ángel
A1 Sánchez Sánchez, Marta
A1 Suárez Llorens, Alfonso
A2 Estadística e Investigación Operativa
K1 Bayesian inference
K1 modeling epidemics
K1 non-homogeneous poisson process
K1 Gompertz curve
K1 inverse Gaussian
AB 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.
PB MDPI
SN 2227-7390
YR 2021
FD 2021-02
LK http://hdl.handle.net/10498/24604
UL http://hdl.handle.net/10498/24604
LA eng
NO Ministerio de Economía y Competitividad. Gobierno de España; 2014-2020 ERDF Operational Programme;  Consejería de Economía, Conocimiento, Empresas y Universidad.Junta de Andalucía
DS Repositorio Institucional de la Universidad de Cádiz
RD 11-nov-2026