COVID-19 vaccination performance of the U.S. states: a hybrid model of DEA and ensemble machine learning methods

Vaccination is seen as the most promising one among the efforts to stop COVID-19 and the U.S. government has given great importance to vaccination. However, which states have performed well in administering COVID-19 vaccines and which have not is an open significant question. Another important question is what makes a state more successful than others when evaluating vaccination performance. To answer both of these questions, we proposed a hybrid method that consists of Data Envelopment Analysis and Ensemble ML Methods. DEA was employed to find the vaccine efficiency of the states using the data aggregated from counties. ML techniques are then applied for the vaccine efficiency prediction and understanding the significance of the variables in the prediction. Our findings revealed that there are considerable differences between U.S. States’ performance and only 16 of the states were efficient in terms of their vaccination performance. Furthermore, Light GBM, Random Forest and XGBoost models provided the best results among the five ensemble machine learning methods that were applied. Therefore, an information fusion-based sensitivity analysis method was used to combine the results of each ML technique and ascertain the relative significance of the factors in the prediction of the efficiency. As the findings for factors affecting vaccination performance, percentage of vaccine doses delivered and COVID-19 deaths were found to be the major influential factors on the prediction of the efficiency and percentage of fully vaccinated people, the number of healthcare employees and human development index followed these variables.