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.
Highlights
In December 2019, the number of pneumonia cases inexplicably increased in China.Later, scientists discovered that they were caused by a novel kind of coronavirus, calledSARS-CoV-2, which appeared for the first time in Wuhan, China, see [1,2]
The COVID-19, the disease related to this new coronavirus, has had a huge impact in human health and social life all over the world, even more than some other infectious diseases occurred in recent years
It is clear that the Gompertz curve considers some initial counts at time zero and we should take in account that N (0) is assumed to be zero in a non-homogeneous Poisson process (NHPP) process
Summary
In December 2019, the number of pneumonia cases inexplicably increased in China. Later, scientists discovered that they were caused by a novel kind of coronavirus, called. Bayesian methods provide an excellent theoretical framework for analyzing experimental data and the main key of its success lies on its ability to incorporate prior knowledge about the quantity of interest as a distribution function. In this case, there exists a lot of information about the new coronavirus during the pandemic which is worth to take into account as well as the behaviour of other types of coronavirus. The decision makers have access to external information such as expert views and past studies or data from other locations This previous knowledge is incorporated into the Bayesian analysis as the prior distribution. We find a rapid increase in the number of publications related to model COVID-19 using Bayesian techniques in literature, see for example [17,18,19,20,21]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
