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

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URI: https://hdl.handle.net/10016/33307
ISSN: 2227-7390
DOI: https://doi.org/10.3390/math9030228
UXXI: AR/0000028264
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Bayesian_Mathematics_2021.pdf (1.24 MB)
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2021-02-01
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MDPI
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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.
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This article belongs to the Special Issue Stochastic Models with Applications
Keywords
Bayesian inference, Gompertz curve, Inverse gaussian, Modeling epidemics, Non-homogeneous poisson process
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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.
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