Use of econometrics and machine learning models to predict the number of new cases per day of COVID-19 - Silva, Roberto; Barreira, Bruna; Xavier, Fernando; Saraiva, Antonio; Cugnasca, Carlos (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2020: Anais do XX Simpósio Brasileiro de Computação Aplicada à SaúdeDO - 10.5753/sbcas.2020.11525, 2020)
The COVID-19 pandemics will impact the demand for healthcare severely. It is essential to continually monitor and predict the expected number of new cases for each country. We explored the use of econometrics, machine learning, and ensemble models to predict the number of new cases per day for Brazil, China, Italy, and South Korea. These models can be used to make predictions in the short term, complementing the epidemiological models. Our main findings were: (i) there is no single best model for all countries; (ii) ensembles can, in some instances, improve the results of individual models; and (iii) the ML models had worse results due to the lack of data.