COVID-19 ölüm oranında etkili olan risk faktörlerinin Dematel yöntemi ile incelenmesi

Yıl 2021, , 2151 - 2166, 02.09.2021

Gözde Şen Elif Demirel Selen Avcı , Zerrin Aladağ

https://doi.org/10.17341/gazimmfd.749133

Cited By: 4

Öz

Dünya, 2020 yılı başından itibaren insan sağlığı için ciddi bir tehdit oluşturan Covid-19 ile karşı karşıya kalmıştır. Covid-19 kaynaklı ölümler üzerinde etkili faktörlerin belirlenmesi virüsle mücadele kapsamında oldukça önemli olup birçok araştırmaya konu olmuştur. Çok ölçütlü karar verme yöntemleri, karar problemi üzerinde etkili olabilecek tüm faktörleri birlikte değerlendirme olanağı sağlayarak karar vericinin etkin sonuca ulaşmasını destekleyen yöntemlerdir. Bu çalışmada, Covid-19 hastalığının risk grubunu oluşturan faktörler çok ölçütlü karar verme yapısına uygun olarak incelenmiştir. Bir problemi etkileyen faktörlerin etkileme gücünü ölçebilen yöntemlerden biri olan DEMATEL (Decision Making Trial and Evaluation Laboratory) yöntemi yardımıyla genel kabul görmüş faktörlerin farklı bir açıdan değerlendirilmesi amaçlanmıştır. Literatürden çalışmanın amacına uygun veriler derlenmiş ve çeşitli kaynaklarda referans olarak verilen faktörler tespit edilmiştir. Ölümler üzerinde etkili olduğu bilinen bu risk faktörlerinin farklı eşik değerleri için sayısal analizleri yapılarak birbirlerini etkileme düzeyleri araştırılmıştır. Eşik değerler belirlenirken virüsün yayılma seyri olan göz önünde bulundurulmuş ve farklı periyotlar için uygulanacak stratejiler bakımından baz alınabilecek sonuçlar üzerinde durulmuştur. Son olarak, çok ölçütlü analiz sonuçları DEMATEL yönteminin temel prensiplerine uygun olarak yorumlanmıştır.

Anahtar Kelimeler

Covid-19, risk faktörleri, DEMATEL, çok kriterli karar verme (ÇKKV)

Kaynakça

• [1] Şenol C., Bilsel A., Status of covid-19 (coronavirus) pandemic in Turkey and future status analysis according to algorithmic calculation, Joshas, 6 (25), 535-546, 2020.
• [2] Petrosillo N., Viceconte G., Ergonul O., Ippolito G., Petersen E., COVID-19, SARS and MERS: are they closely related?, Clin Microbiol Infec, 26 (6), 729-734, 2020.
• [3] World Health Organization. Novel Coronavirus (2019-nCoV) Situation Report. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200211-sitrep-22-ncov.pdf?sfvrsn=fb6d49b1_2 22. Yayın tarihi Şubat 11, 2020. Erişim tarihi Mayıs 11, 2020.
• [4] World Health Organization. Novel coronavirus (2019-nCoV) Situation Report. https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-March-2020. Yayın tarihi Mart 11, 2020. Erişim tarihi Mayıs 11, 2020.
• [5] Yuan J., Li M., Lv G., Lu Z. K. Monitoring transmissibility and mortality of COVID-19 in Europe, Int J Infect Dis, 95, 311-315, 2020.
• [6] Utkucan Ş., Tezcan Ş., Forecasting the cumulative number of confirmed cases of COVID-19 in Italy, UK and USA using fractional nonlinear grey Bernoulli model, Chaos Soliton Fract, 2020.
• [7] Yazdi M., Khan F., Abbassi R., Rusli R., Improved DEMATEL methodology for effective safety management decision-making, Safety Sci, 127, 1-17, 2020.
• [8] Barak S., Mokfi T., Evaluation and selection of clustering methods using a hybrid group MCDM, Expert Syst Appl, 138, 1-19, 2019.
• [9] Aksakal E., Dağdeviren, M., Talent management based personnel assignment model and solution proposal, Journal of the Faculty of Engineering and Architecture of Gazi University, 30 (2), 249-262, 2015.
• [10] Du Y. W., Zhou W., New improved DEMATEL method based on both subjective experience and objective data, Eng Appl Artif Intel, 83, 57-71, 2019.
• [11] Oleśków-Szłapka J., Wojciechowski H., Domański R., Pawłowski, G., Logistics 4.0 maturity levels assessed based on gdm (grey decision model) and artificial intelligence in logistics 4.0-trends and future perspective, Procedia Manufacturing, 39, 1734-1742, 2019.
• [12] Torbacki W., Kijewska K., Identifying Key Performance Indicators to be used in Logistics 4.0 and Industry 4.0 for the needs of sustainable municipal logistics by means of the DEMATEL method. Transportation Research Procedia, 39, 534-543, 2019.
• [13] Haleem A., Khan S., Khan M. I.,Traceability implementation in food supply chain: a grey-DEMATEL approach. Information Processing in Agriculture, 6 (3), 335-348, 2019.
• [14] Asad M. M., Mohajerani N. S., Nourseresh M., Prioritizing factors affecting customer satisfaction in the internet banking system based on cause and effect relationships. Procedia Economics and Finance, 36 (16), 210-219, 2016.
• [15] Aksakal E., Dağdeviren M., An integrated approach for personel selection with dematel and anp method, Journal of the Faculty of Engineering and Architecture of Gazi University, 25 (4), 905-913, 2010.
• [16] Jiang S., Shi H., Lin W., Liu H. C., A large group linguistic Z-DEMATEL approach for identifying key performance indicators in hospital performance management, Appl Soft Comput, 86, 1-12, 2020.
• [17] Mahmoudi S., Jalali A., Ahmadi M., Abasi P., Salari N., Identifying critical success factors in Heart Failure Self-Care using fuzzy DEMATEL method, Appl Soft Comput, 84, 1-16, 2019.
• [18] Shieh J. I., Wu H. H., Huang K. K., A DEMATEL method in identifying key success factors of hospital service quality, Knowledge-Based Systems, 23 (3), 277-282, 2010.
• [19] Majumder P., Biswas P., Majumder S., Application of new topsis approach to identify the most significant risk factor and continuous monitoring of death of Covid-19, Electron J Gen Med, 17 (6), 1-12, 2020.
• [20] Mohammed M. A., Abdulkareem K. H., Al-Waisy A. S, Mostafa S. A., Al-Fahdawi S., Dinar A. M., ... , Arbaiy N., Benchmarking methodology for selection of optimal Covid-19 diagnostic model based on entropy and topsis methods. IEEE Access, 2020.
• [21] Requia W. J., Kondo E. K., Adams M. D., Gold D. R., Struchiner C. J., Risk of the Brazilian health care system over 5572 municipalities to exceed health care capacity due to the 2019 novel coronavirus (COVID-19), Sci Total Environ, 730, 1-8, 2020.
• [22] Yang Z., Li X., Garg H., Qi M., Decision support algorithm for selecting an antivirus mask over covid-19 pandemic under spherical normal fuzzy environment, Int J Env Res Pub He, 17 (10), 3407, 1-27, 2020.
• [23] Hashemkhani Zolfani S., Yazdani M., Ebadi Torkayesh A., Derakhti A., Application of a gray-based decision support framework for location selection of a temporary hospital during Covid-19 pandemic, Symmetry, 12 (6), 886, 1-15, 2020.
• [24] Mishra M., Mishra, P., Prioritizing financial crises due to covid-19: an economic safety and sustainability approach in India, IJSDA, 10 (1), 1-11, 2020.
• [25] Ren Z., Liao H., Liu Y., Generalized Z-numbers with hesitant fuzzy linguistic information and its application to medicine selection for the patients with mild symptoms of the COVID-19, Comput Ind Eng, 145, 1-9, 2020.
• [26] Wu H. H., Chen H. K., Shieh J. I., Evaluating performance criteria of employment service outreach program personnel by DEMATEL method, Expert Syst Appl, 37 (7), 5219-5223, 2010.
• [27] Kabadayı N., Dağ S., Dealership performance evaluation in supply chain with DEMATEL and ELECTRE methods, Pamukkale University Journal of Engineering Sciences, 26 (1), 241-253, 2020.
• [28] Karaoğlan S., Şahin S., An integrated approach to the purchase problem of businesses with DEMATEL and AHP methods: a dslr camera example, Journal of Business Research, 8 (2), 359-375, 2016.
• [29] Wuhan Municipal Health Commission. Report of clustering pneumonia of unknown etiology in Wuhan City. https://www.who.int/csr/don/05-january-2020-pneumonia-of-unkown-cause-china/en/. Yayın tarihi Aralık 2019. Erişim tarihi Mayıs 9, 2020.
• [30] Türkiye Bilimler Akademisi, Covid-19 Global Outbreak Assessment Report, Türkiye Bilimler Akademisi Yayınları, TÜBA Raporları No: 34 ISBN: 978-605-2249-43-7, Ankara, 2020. [31] Wu Z., McGoogan J. M., Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention, Jama, 323(13), 1239-1242, 2020.
• [32] Wölfel R., Corman V. M., Guggemos W., Seilmaier M., Zange S., Müller M. A., ... Hoelscher M., Virological assessment of hospitalized patients with COVID-2019, Nature, 581, 465-469, 2020.
• [33] Huang C., Wang Y., Li X., Ren L., Zhao J., Hu Y., ... Cheng, Z, Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China, The Lancet, 395(10223), 497-506, 2020.
• [34] Choi K. W., Chau T. N., Tsang O., Tso E., Chiu M. C., Tong W. L., ... Lam W, Outcomes and prognostic factors in 267 patients with severe acute respiratory syndrome in Hong Kong., Ann Intern Med, 139 (9), 715-723, 2003.
• [35] Hong K. H., Choi J. P., Hong S. H., Lee J., Kwon J. S., Kim S. M., ... Shin E. C., Predictors of mortality in Middle East respiratory syndrome (MERS), Thorax, 73 (3), 286-289, 2018.
• [36] Duru S, COVID-19 in elderly patients, Eurasian J Pulmonol, 128-134, 2020.
• [37] Zhavoronkov A., Geroprotective and senoremediative strategies to reduce the comorbidity, infection rates, severity, and lethality in gerophilic and gerolavic infections, Aging, 12 (8), 6492-6510, 2020.
• [38] Worldometer. Age, Sex, Existing Conditions of COVID-19 Cases and Deaths. https://www.worldometers.info/coronavirus/coronavirus-age-sex-demographics/. Yayın tarihi 2020. Erişim tarihi Mayıs 9, 2020.
• [39] Globalhealth. Sex, Gender And Covıd-19. https://globalhealth5050.org/covid19/#1586352650173-d9a8b64b-670a. Yayın tarihi 2020. Erişim tarihi Mayıs 9, 2020.
• [40] Zhao Y, Zhao Z, Wang Y, Zhou Y, Ma Y, Zuo W, Single-cell RNA expression profil-ing of ACE2, the putative receptor of Wuhan 2019-nCov, bioRxiv 2020, (ön baskı).
• [41] Zhang J. J., Dong X., Cao Y. Y., Yuan Y. D., Yang Y. B., Yan Y. Q., ... Gao Y. D., Clinical characteristics of 140 patients infected with SARS‐CoV‐2 in Wuhan, China, Allergy, 00, 1-12, 2020
• [42] Guan W. J., Ni Z. Y., Hu Y., Liang W. H., Ou C. Q., He J. X., ... Du B., Clinical characteristics of coronavirus disease 2019 in China, New Engl J Med, 382 (18), 1708-1720, 2020.
• [43] Cai G., Bulk and single-cell transcriptomics identify tobacco-use disparity in lung gene expression of ACE2, the receptor of 2019-nCov. MedRxiv, 2020, (ön baskı).
• [44] Guan W. J., Liang W. H., Zhao Y., Liang H. R., Chen Z. S., Li Y. M., ... Ou C. Q., Comorbidity and its impact on 1590 patients with Covid-19 in China: A Nationwide Analysis, European Respiratory Journal, 55 (5), 1-14, 2020.
• [45] Liu W., Tao Z. W., Wang L., Yuan M. L., Liu K., Zhou L., ... Ming Y., Analysis of factors associated with disease outcomes in hospitalized patients with 2019 novel coronavirus diseasei 1-7, Chinese Med J-Peking, 2020.
• [46] Zhou F., Yu T., Du R., Fan G., Liu Y., Liu Z., ... Guan L., Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study, The Lancet, 395, 1054-1062, 2020.
• [47] Saez M., Tobias A., Varga D., Barceló M. A., Effectiveness of the measures to flatten the epidemic curve of COVID-19. The case of Spain, Sci Total Environ, 727, 1-7, 2020.