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.
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