This work proposes a method to rank the contribution of different strategies to contain the evolution of the COVID-19 pandemic in different states of Brazil, in the pre- and post-vaccination periods. The proposed method included the automatic learning of regression models using the XGBoost machine learning algorithm, and applied Shapley s cooperative game theory to quantify the contribution of the analyzed characteristics to the target variable. To interpret the model globally, the SHapley Additive exPlanations (SHAP) was used, which is an algorithm based on Shapley s theory. The evaluation results point to its efficacy to quantify the contribution of each variable in a robust way, and reveal that the percentages of first and second dose vaccination coverage, in addition to the closing of schools, were the measures that had the greatest contribution in the evolution of the number of cases and deaths due to COVID-19. The weighting of variables can help the actors responsible in the elaboration of public policies to minimize the socioeconomic effects in their regions, since Brazil is a country that has extreme social inequality.