COVID-19 Crisis Management: Global Appraisal using Two-Stage DEA and Ensemble Learning Algorithms

Document Type : Article


Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran


Due to the rapid growth of COVID-19 data, this paper investigated an integrated approach for performance evaluation of countries at any time of the COVID-19 pandemic. First, the strategies implemented in countries were summarized in three systems: prevention, infection detection, and medical. Then, the input-output of the systems was identified. In Phase 1, after variable selection (tests, total cases, active cases, recovered cases, and deaths), data were collected for 100 countries with the highest infected cases by June 21, 2021. Then, mathematical modeling of two-stage data envelopment analysis with desirable-undesirable variables was performed using three basic ideas: independent, connected, and relational. By solving the relational model, the efficiency scores of the countries were obtained, and they were categorized into four classes based on these results. In Phase 2, 80% of the data were considered as training samples to generate a machine learning model via ensemble methods (i.e., Bag, Adaptive Boost, and Random Under-Sampling Boost). In Phase 3, the class of test samples was predicted using the optimal ensemble model. The results showed that in a small dataset, the Bag algorithm had 95% accuracy in predicting the class of test samples.


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