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The SEIRD (Susceptible, Exposed, Infected, Recovered, and Dead) model is a mathematical model based on dynamic equations; widely used for characterization of the COVID-19 pandemic. In this paper, a different approach has been discussed, which is the development of predictive models for the SEIRD variables that have been based on the historical data collected, and the context variables to where this model has been applied to. Particularly, the context variables examined in this paper include total population, number of people over 65 years old, poverty index, morbidity rates, average age, and population density. For the construction of the SEIRD predictive models, this study encompasses a deep analysis of the dependence of these variables and also, their relationship with the context variables. Hence, before the development of predictive models using machine learning techniques, a methodology to …