Analysis of the socioeconomic impact due to COVID-19 using a deep clustering approach.

One of the main problems that countries are currently having is being able to measure the impact of the pandemic in other areas of society (for example, economic or social). In that sense, being able to combine variables about the behavior of COVID-19 with other variables in the environment, to build models about its impact, which help the decision-making of national authorities, is a current challenge. In this sense, this work proposes an approach that allows monitoring the socioeconomic behavior of the regions/departments of a country (in this case, Colombia) due to the effect of COVID-19.

Machine learning models for the prediction of the SEIRD variables for the COVID-19 pandemic based on a deep dependence analysis of variables.

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.