Xu Yuan, Park Yong Shin, Park Ju Dong
School of Maritime Economics and Management, Dalian Maritime University, 1 Linghai Road, Dalian 116026, China.
Department of Marketing, Operations, and Analytics, Bill Munday School of Business, St. Edward's University, 3001 South Congress, Austin, TX 78704, USA.
Healthcare (Basel). 2021 Mar 3;9(3):268. doi: 10.3390/healthcare9030268.
Measuring the U.S.'s COVID-19 response performance is an extremely important challenge for health care policymakers. This study integrates Data Envelopment Analysis (DEA) with four different machine learning (ML) techniques to assess the efficiency and evaluate the U.S.'s COVID-19 response performance. First, DEA is applied to measure the efficiency of fifty U.S. states considering four inputs: number of tested, public funding, number of health care employees, number of hospital beds. Then, number of recovered from COVID-19 as a desirable output and number of confirmed COVID-19 cases as a undesirable output are considered. In the second stage, Classification and Regression Tree (CART), Boosted Tree (BT), Random Forest (RF), and Logistic Regression (LR) were applied to predict the COVID-19 response performance based on fifteen environmental factors, which were classified into social distancing, health policy, and socioeconomic measures. The results showed that 23 states were efficient with an average efficiency score of 0.97. Furthermore, BT and RF models produced the best prediction results and CART performed better than LR. Lastly, urban, physical inactivity, number of tested per population, population density, and total hospital beds per population were the most influential factors on efficiency.
对医疗保健政策制定者而言，衡量美国应对新冠疫情的表现是一项极其重要的挑战。本研究将数据包络分析（DEA）与四种不同的机器学习（ML）技术相结合，以评估效率并评价美国应对新冠疫情的表现。首先，应用DEA衡量美国五十个州的效率，考虑四个投入因素：检测人数、公共资金、医护人员数量、医院病床数量。然后，将新冠康复人数作为期望产出，将新冠确诊病例数作为非期望产出。在第二阶段，应用分类与回归树（CART）、增强树（BT）、随机森林（RF）和逻辑回归（LR），基于十五个环境因素预测新冠疫情应对表现，这些因素分为社会距离、卫生政策和社会经济措施。结果显示，23个州效率较高，平均效率得分为0.97。此外，BT和RF模型产生了最佳预测结果，CART的表现优于LR。最后，城市、身体不活动、人均检测数、人口密度和人均医院病床总数是对效率影响最大的因素。
