ASSESSING THE CONTRIBUTION OF CONTAINMENT MEASURES, VACCINATION COVERAGE AND MOBILITY IN THE EVOLUTION OF DEATHS FROM COVID-19 IN BRAZIL

Abstract

This work proposes a method to rank the contribution of containment measures, vaccination coverage and mobility to contain the evolution of the COVID-19 pandemic in different states of Brazil. The proposed method applies the automatic learning of regression models using decision trees through the XGBoost algorithm. To interpret it globally, the SHapley Additive exPlanations (SHAP) was used, which is an algorithm based on Shapley's cooperative game theory to quantify the contribution of the analyzed characteristics to the evolution of the target variable (deaths). The evaluation results of the method indicated its efficiency to quantify the contribution of each variable in a robust way. It reveals that the percentages of vaccine coverage of the first and second dose, in addition to the closure of schools, were the measures that had the greatest contribution to the evolution of the number of deaths from COVID-19. The weighting of the variables can help the responsible actors in the elaboration of public policies to minimize the socioeconomic effects in their regions. Since, in countries with great social inequality, the use of only a few more efficient measures would be less harmful than a lockdown, as it would be extremely harmful to the quality of life of these poorer populations.

Keywords

• covid-19
• xgboost
• shapley values
• interpretability