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A phenomenological model for COVID‐19 data taking into account neighboring‐provinces effect and random noise

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  • Julia Calatayud
  • Marc Jornet
  • Jorge Mateu

Abstract

We model the incidence of the COVID‐19 disease during the first wave of the epidemic in Castilla‐Leon (Spain). Within‐province dynamics may be governed by a generalized logistic map, but this lacks of spatial structure. To couple the provinces, we relate the daily new infections through a density‐independent parameter that entails positive spatial correlation. Pointwise values of the input parameters are fitted by an optimization procedure. To accommodate the significant variability in the daily data, with abruptly increasing and decreasing magnitudes, a random noise is incorporated into the model, whose parameters are calibrated by maximum likelihood estimation. The calculated paths of the stochastic response and the probabilistic regions are in good agreement with the data.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:stanee:v:77:y:2023:i:2:p:146-155
    DOI: 10.1111/stan.12278
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    References listed on IDEAS

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    1. 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.
    2. Domingo Benítez & Gustavo Montero & Eduardo Rodríguez & David Greiner & Albert Oliver & Luis González & Rafael Montenegro, 2020. "A Phenomenological Epidemic Model Based On the Spatio-Temporal Evolution of a Gaussian Probability Density Function," Mathematics, MDPI, vol. 8(11), pages 1-22, November.
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