Publication: A bayesian model of covid-19 cases based on the gompertz curve
Loading...
Identifiers
Publication date
2021-02-01
Defense date
Advisors
Tutors
Journal Title
Journal ISSN
Volume Title
Publisher
MDPI
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.
Description
This article belongs to the Special Issue Stochastic Models with Applications
Keywords
Bayesian inference, Gompertz curve, Inverse gaussian, Modeling epidemics, Non-homogeneous poisson process
Bibliographic citation
Berihuete, N., Sánchez-Sánchez, M. & Suárez-Llorens, A. (2021). A Bayesian Model of COVID-19 Cases Based on the Gompertz Curve. Mathematics, 9(3), 228.