Berihuete, ÁngelSánchez Sánchez , MartaSuárez-Llorens, Alfonso2021-09-232021-09-232021-02-01Berihuete, N., Sánchez-Sánchez, M. & Suárez-Llorens, A. (2021). A Bayesian Model of COVID-19 Cases Based on the Gompertz Curve. Mathematics, 9(3), 228.2227-7390https://hdl.handle.net/10016/33307This article belongs to the Special Issue Stochastic Models with ApplicationsThe 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.16eng© 2021 by the authors.Atribución 3.0 EspañaBayesian inferenceGompertz curveInverse gaussianModeling epidemicsNon-homogeneous poisson processresearch articleMatemáticashttps://doi.org/10.3390/math9030228open access2283Mathematics9AR/0000028264