Discrete Gompertz and Generalized Logistic models for early monitoring of the COVID-19 pandemic in Cuba


modelos fenomenológicos discretos
ecuaciones diferenciales de primer orden
previsión de enfermedades a corto plazo
método Bootstrap
pandemia discrete phenomenological models
first-order difference equations
short-term disease forecasting
bootstrap method

How to Cite

Pérez Maldonado, M. T., Bravo Castillero, J., Mansilla, R., & Caballero Pérez, R. Óscar. (2022). Discrete Gompertz and Generalized Logistic models for early monitoring of the COVID-19 pandemic in Cuba. Nova Scientia, 14(29). https://doi.org/10.21640/ns.v14i29.3162


The COVID-19 pandemic has motivated a resurgence in the use of phenomenological growth models for predicting the early dynamics of infectious diseases. These models assume that time is a continuous variable, whereas in the present contribution the discrete versions of Gompertz and Generalized Logistic models are used for early monitoring and short-term forecasting of the spread of an epidemic in a region. The time-continuous models are represented mathematically by first-order differential equations, while their discrete versions are represented by first-order difference equations that involve parameters that should be estimated prior to forecasting. The methodology for estimating such parameters is described in detail. Real data of COVID-19 infection in Cuba is used to illustrate this methodology. The proposed methodology was implemented for the first thirty-five days and was used to predict accurately the data reported for the following twenty days.



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