Zoie Rӑdulescu Constanța, Marius Rӑdulescu, Gheorghițӑ Zbӑganu, Radu Boncea
National Institute for Research and Development in Informatics, 8-10, Mareşal Averescu, Bucharest, 011455, Romania.
"Gheorghe Mihoc-Caius Iacob" Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy, Calea 13 Septembrie, No.13, Bucharest, 050711, Romania.
Procedia Comput Sci. 2022;199:149-156. doi: 10.1016/j.procs.2022.01.019. Epub 2022 Feb 3.
The pandemic generated by the Sars Cov 2 corona virus is monitored, at the level of each country, every day, by several COVID-19 indicators. The present paper proposes a Group Multi-Criteria (GMC) approach for the development of a country COVID-19 indicator called COPACOV (COuntry Performance Against COVID-19) indicator. It is calculated starting from several country COVID-19 indicators measured separately, in a set a countries. COPACOV can identify which countries are more vulnerable to COVID-19 illnesses from several points of view taken together. The GMC approach is based on a hybrid method composed from the Group Analytic Hierarchy Process (GAHP) weighting method and from the Multicriteria Optimization and Compromise Solution (VIKOR) multi-criteria method. The aggregation (consensus) of the experts' opinions, in the GAHP method is calculated with the geometric mean method. The VIKOR method uses the weights calculated by the GAHP method and calculates the COPACOV indicators. The proposed GMC approach is applied in a case study for a set of COVID-19 indicators and a set of Eastern European countries.
由严重急性呼吸综合征冠状病毒2（Sars Cov 2）引发的大流行，在每个国家层面，每天都由若干新冠病毒疾病指标进行监测。本文提出一种群组多准则（GMC）方法，用于开发一种名为“抗击新冠病毒国家表现（COPACOV）指标”的国家新冠病毒疾病指标。它是从一组国家中分别测量的若干国家新冠病毒疾病指标开始计算的。COPACOV能够从多个综合角度识别哪些国家更容易受到新冠病毒疾病的影响。GMC方法基于一种混合方法，该方法由群组层次分析法（GAHP）加权法和多准则优化与折衷解（VIKOR）多准则方法组成。在GAHP方法中，专家意见的汇总（共识）采用几何平均法计算。VIKOR方法使用GAHP方法计算的权重来计算COPACOV指标。所提出的GMC方法应用于一个案例研究，涉及一组新冠病毒疾病指标和一组东欧国家。
