Research Article Capacity Evaluation of Diagnostic Tests For COVID-19 Using Multicriteria Decision-Making Techniques Murat Sayan, 1,2 Figen Sarigul Yildirim, 3 Tamer Sanlidag, 2,4 Berna Uzun, 2,5 Dilber Uzun Ozsahin , 2,6 and Ilker Ozsahin 2,6 1 Faculty of Medicine, Clinical Laboratory, PCR Unit, Kocaeli University, Kocaeli, Turkey 2 DESAM Institute, Near East University, Nicosia/TRNC, Mersin-10, 99138, Turkey 3 Health Science University, Antalya Education and Research Hospital, Department of Infectious Diseases and Clinical Microbiology, Antalya 07050, Turkey 4 Department of Medical Microbiology, Manisa Celal Bayar University, Manisa, Turkey 5 Department of Mathematics, Near East University, Nicosia/TRNC, Mersin-10, 99138, Turkey 6 Department of Biomedical Engineering, Faculty of Engineering, Near East University, Nicosia/TRNC, Mersin-10, 99138, Turkey Correspondence should be addressed to Ilker Ozsahin; ilker.ozsahin@neu.edu.tr Received 11 May 2020; Revised 22 June 2020; Accepted 7 July 2020; Published 6 August 2020 Guest Editor: Plácido R. Pinheiro Copyright © 2020 Murat Sayan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In December 2019, cases of pneumonia were detected in Wuhan, China, which were caused by the highly contagious coronavirus. This study is aimed at comparing the confusion regarding the selection of effective diagnostic methods to make a mutual comparison among existing SARS-CoV-2 diagnostic tests and at determining the most effective one. Based on available published evidence and clinical practice, diagnostic tests of coronavirus disease (COVID-19) were evaluated by multi-criteria decision-making (MCDM) methods, namely, fuzzy preference ranking organization method for enrichment evaluation (fuzzy PROMETHEE) and fuzzy technique for order of preference by similarity to ideal solution (fuzzy TOPSIS). Computerized tomography of chest (chest CT), the detection of viral nucleic acid by polymerase chain reaction, cell culture, CoV-19 antigen detection, CoV-19 antibody IgM, CoV-19 antibody IgG, and chest X-ray were evaluated by linguistic fuzzy scale to compare among the diagnostic tests. This scale consists of selected parameters that possessed different weights which were determined by the experts’ opinions of the field. The results of our study with both proposed MCDM methods indicated that the most effective diagnosis method of COVID-19 was chest CT. It is interesting to note that the methods that are consistently used in the diagnosis of viral diseases were ranked in second place for the diagnosis of COVID-19. However, each country should use appropriate diagnostic solutions according to its own resources. Our findings also show which diagnostic systems can be used in combination. 1. Introduction After cases of pneumonia of unknown cause were detected in Wuhan, China, in December 2019, a new coronavirus was isolated from human airway epithelial cells and was named severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2), which is responsible for coronavirus disease (COVID-19) [1]. SARS-CoV-2 is also a member of the coro- navirus family that includes Middle East Respiratory Syn- drome- (MERS-) CoV and SARS-CoV, which infect humans [1, 2]. Wild animals are the source of the infection. According to phylogenetic analysis of full genome sequencing, the coronavirus that causes COVID-19 is a beta- coronavirus in the same subgenus clade as SARS-CoV-2. The structure of the receptor binding site for cell entry is similar and uses the angiotensin-converting enzyme 2 receptor found in the epithelial cells of the alveoli used by SARS- CoV-2 [3]. The International Committee on Taxonomy of Viruses has proposed that this virus be designated as SARS- CoV-2 [3, 4]. The main mode of transmission is via person-to-person spread. When an infected person coughs, sneezes, or speaks, Hindawi Computational and Mathematical Methods in Medicine Volume 2020, Article ID 1560250, 8 pages https://doi.org/10.1155/2020/1560250