大流行两年后利用数据包络分析法确定 15 个经合组织国家应对 Covid-19 的效率

Esra Polat
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引用次数: 0

摘要

Covid-19 于 2019 年 12 月在中国武汉市出现,很快蔓延至全球,随后被世界卫生组织(WHO)认定为大流行病。如今,应对 "Covid-19 "仍是各国的重要课题之一。本研究采用数据包络分析法(DEA)分析了 15 个经合组织国家应对该流行病的相对效率。当前卫生支出(占国内生产总值的百分比)、护士和助产士(每千人)、医院床位(每千人)、医生(每千人)变量被用作投入;总病例数(每百万人)、死亡人数(每百万人)和检测次数(每百万人)变量被用作产出。使用以产出为导向的 Charnes、Cooper 和 Rhodes(CCR)以及 Banker、Charnes 和 Cooper(BCC)DEA 模型,确定规模效率值,并对效率低下的国家提出潜在的改进建议。结果发现,奥地利、墨西哥、韩国、西班牙和土耳其的技术效率、总体效率和规模效率都相对较高。研究还得出结论,低效率国家必须有效利用其输入变量,Covid-19 检验的次数对于一个国家相对于 Covid-19 的效率非常重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Determining Efficiency of 15 OECD Countries Coping with Covid-19 using Data Envelopment Analysis after 2 Years of Pandemic
Covid-19, emerged in Wuhan, a city of China, at the date of December 2019, it spread to the entire world soon and then by the World Health Organization (WHO) accepted as a pandemic. Today, still the coping with Covid-19 is the one of the significant topics of countries. In this study, the relative efficiencies of 15 OECD countries for coping with the pandemic is analyzed with Data Envelopment Analysis (DEA). Current health expenditure (% of GDP), Nurses and midwives (per 1 thousand people), Hospital beds (per 1 thousand people), Physicians (per 1 thousand people) variables used as inputs; Number of Total Cases (per 1 million people), Number of Deaths (per 1 million people) and Number of Tests (per 1 million people) variables used as outputs. Output oriented Charnes, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BCC) DEA models are used, scale efficiency values are determined and potential improvement suggestions are given for inefficient countries. Austria, Mexico, South Korea, Spain and Türkiye are found to be both relative technical, total and scale efficient. It is also concluded that inefficient countries must use their input variables efficiently and number of Covid-19 tests is important for a country’s efficiency against Covid-19.
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