An incremental learning approach to prediction models of SEIRD variables in the context of the COVID-19 pandemic

Abstract

Several works have proposed predictive models of the SEIRD (Susceptible, Exposed, Infected, Recovered, and Dead) variables to characterize the pandemic of COVID-19. One of the challenges of these models is to be able to follow the dynamics of the disease to make more precise predictions. In this paper, we propose an approach based on incremental learning to build predictive models of the SEIRD variables for the COVID-19 pandemic. Our incremental learning approach is a dynamic ensemble method based on a bagging scheme that allows the addition of new models or the updating of incremental models. The article proposes an incremental learning architecture composed of two components. The first component carries out an analysis of the interdependencies of the SEIRD variables and the second component is an incremental learning model that builds/updates the predictive models. The paper analyses the quality of the predictive models of our incremental learning approach using data of the COVID-19 from Colombia, and shows interesting results about the predictions of the SEIRD variables. These results are compared with an incremental learning approach based on random forests.