On the Probabilistic Extension of the Classical Epidemiological Compartmental Model

Abstract

AbstractThe worldwide SARS-Cov2 outbreak has shown the importance of the science, and surprisingly thanks to the classical models for infectious diseases used by scientists, where the mathematics has played an important role, to study and predict the short-, medium-, and long-term behaviour of the pandemic. The classical SIR and SEIR models have been revisited and several papers have been appeared with new upgraded proposals. Statistical models of machine learning approaches have emerged as possible alternatives to the classical models. Our proposal, in order to create a probabilistic SEIR model (pSEIR), is combining the underlying ideas of the classical model introducing random effects for the changes into compartments. With this uncertainty in the behaviour of the susceptible, exposed, infectious, and recovered individuals we obtain a model that allows to simulate scenarios in order to predict the future evolution of the infectious disease. Our results allows to recover the curves of the deterministic SEIR model, but in a probabilistic framework.