A bayesian model of covid-19 cases based on the gompertz curve Articles uri icon

authors

  • Berihuete, Angel
  • SANCHEZ SANCHEZ, MARTA
  • Suarez Llorens, Alfonso

publication date

  • February 2021

start page

  • 228

issue

  • 3

volume

  • 9

International Standard Serial Number (ISSN)

  • 2227-7390

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.

subjects

  • Mathematics

keywords

  • bayesian inference; gompertz curve; inverse gaussian; modeling epidemics; non-homogeneous poisson process