COVID-19 and controlled distance in the state of Rio Grande do Sul: an analysis of coping measures


  • Eduardo Damiani Pavin
  • Luis Henrique Botton
  • Geraldo Edmundo Silva Junior
  • Andre da Silva Pereira
  • Geizi Cássia Bettin do Amarante



COVID-19, coronavirus, government policy, public policy, state and local government: health


The Coronavirus pandemic (COVID-19) started in Wuhan, China, in December 2019. It was disseminated to the globalized world, affecting it in a multifaceted way in its economic, social, political, and, mainly, public health aspects. Therefore, studying the problem from identifying its pandemic characteristics and its main determinants became relevant from the beginning.  This paper used the Machine Learning approach to identify the main variables that reveal policy changes' effect. Then, the methodological approaches revealed the use of LASSO to Linear Regression; LASSO for Multinomial Logistic Regression, Elastic Net Regression; Regression Tree, and Random Forest for the description of the pandemic in the Rio Grande do Sul. The results showed high accuracy in the identification of the relevant variables with a low error rate. Finally, it concluded that the three possible states of recovered, death, and recovering stage described using the machine learning methodology with a high degree of accuracy, attesting the adequacy of the measures adopted in the Rio Grande do Sul.