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A Bayesian Model of COVID-19 Cases Based on the Gompertz Curve

Author

Listed:
  • Ángel Berihuete

    (Dpto. Estadística e Investigación Operativa, Universidad de Cádiz, 11510 Puerto Real, Spain
    These authors contributed equally to this work.)

  • Marta Sánchez-Sánchez

    (IBiDat UC3M-Santander Big Data Institute, Universidad Carlos III de Madrid, 28903 Getafe, Madrid, Spain
    These authors contributed equally to this work.)

  • Alfonso Suárez-Llorens

    (Dpto. Estadística e Investigación Operativa, Universidad de Cádiz, 11510 Puerto Real, Spain
    These authors contributed equally to this work.)

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.

Suggested Citation

  • Ángel Berihuete & Marta Sánchez-Sánchez & Alfonso Suárez-Llorens, 2021. "A Bayesian Model of COVID-19 Cases Based on the Gompertz Curve," Mathematics, MDPI, vol. 9(3), pages 1-16, January.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:3:p:228-:d:486325
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    References listed on IDEAS

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    1. Carpenter, Bob & Gelman, Andrew & Hoffman, Matthew D. & Lee, Daniel & Goodrich, Ben & Betancourt, Michael & Brubaker, Marcus & Guo, Jiqiang & Li, Peter & Riddell, Allen, 2017. "Stan: A Probabilistic Programming Language," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 76(i01).
    2. N. G. Becker & T. Britton, 1999. "Statistical studies of infectious disease incidence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 287-307, April.
    3. Sánchez-Sánchez, M. & Sordo, M.A. & Suárez-Llorens, A. & Gómez-Déniz, E., 2019. "Deriving Robust Bayesian Premiums Under Bands Of Prior Distributions With Applications," ASTIN Bulletin, Cambridge University Press, vol. 49(1), pages 147-168, January.
    4. Se Yoon Lee & Bowen Lei & Bani Mallick, 2020. "Estimation of COVID-19 spread curves integrating global data and borrowing information," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-17, July.
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    Cited by:

    1. Estrada, Ernesto & Bartesaghi, Paolo, 2022. "From networked SIS model to the Gompertz function," Applied Mathematics and Computation, Elsevier, vol. 419(C).
    2. Julia Calatayud & Marc Jornet & Jorge Mateu, 2023. "A phenomenological model for COVID‐19 data taking into account neighboring‐provinces effect and random noise," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 77(2), pages 146-155, May.

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