Covid-19 in Ecuador: Application of data mining
DOI:
https://doi.org/10.33936/isrtic.v6i1.4366Keywords:
Covid-19, Minería de Datos, metodología KDD, algoritmos series-temporalesAbstract
COVID-19 was quickly introduced as a global pandemic, which needs to be addressed with immediate and integrated responses to all national systems that require them. With the advent of COVID-19 the world saw the need for timely responses and data sharing on this and future rapidly spreading global pandemics. This study focuses on predicting the incidence of COVID-19 in
Ecuador. Data mining was performed on records provided by public institutions of the Ecuadorian state with official and updated information on COVID-19 in Ecuador. We experimented with regression and long-term memory models,
obtaining as a result the optimal model to estimate the number of positive cases of COVID-19. For the mathematical model, the mean square error was
used as a performance metric. From the analysis of the data on COVID-19 in Ecuador, the linear regression model predicted the incidence with a mean square error of 0.54, the most effective factors being the incidence of previous days and the population in each of the affected provinces.
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Copyright (c) 2022 Juan-Carlos Zambrano, Patricia Quiroz-Palma, Alex Santamaría-Philco, Willian Zamora
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