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dc.contributor.authorTansel, Yusuf I. C.
dc.date.accessioned2022-06-23T11:18:07Z
dc.date.available2022-06-23T11:18:07Z
dc.date.issued2021
dc.identifier.issn2147-1762en_US
dc.identifier.urihttps://dergipark.org.tr/en/download/article-file/1167071
dc.identifier.urihttp://hdl.handle.net/11727/7132
dc.description.abstractDifficulties to use convenient data during the Severe Acute Respiratory Syndrome Coronavirus2 (SARS-CoV-2) pandemic outbreak and complexities of the problem attitude crucial challenges in infectious disease modelling studies. Motivated by the on-going reach to predict a potential reactivated SARS-CoV-2 (COVID-19), we suggest a prediction model that beyond the clinical characteristics based evaluation approaches. In particular, we developed a possibly available and more efficient prediction model to predict a potential reactivated SARS-CoV-2 (COVID-19) patient. Our paper aims to explore the applicability of a modified Technique for Order Preference by Similarity to Ideal Solutions (MTOPSIS) integrated Design of Experiment (DoE) method to predict a potential reactivated COVID-19 patient in real-time clinical or laboratory applications. The presented novel model may be of interest to the readers studying similar research areas. We illustrate MTOPSIS integrated DoE method by applying it to the COVID-19 pandemic real clinical cases from Wuhan/China-based data. Despite the small sample size, our study provides an encouraging preliminary model framework. Finally, a step by step algorithm is suggested in the study for future research perspectives.en_US
dc.language.isoengen_US
dc.relation.isversionof10.35378/gujs.757464en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectSARS-CoV-2en_US
dc.subjectCOVID-19en_US
dc.subjectLaboratory medicineen_US
dc.subjectDesign of experimenten_US
dc.subjectTOPSIS meta-modelen_US
dc.titleA new DoE-MTOPSIS based prediction model suggestion to capture potential SARS-CoV-2 reactivated patientsen_US
dc.typearticleen_US
dc.relation.journalGAZI UNIVERSITY JOURNAL OF SCIENCEen_US
dc.contributor.departmentBaşkent Üniversitesien_US
dc.identifier.volume34en_US
dc.identifier.issue4en_US
dc.identifier.startpage1051en_US
dc.identifier.endpage1062en_US
dc.identifier.wos000725449700010en_US
dc.identifier.scopus2-s2.0-85122728394en_US
dc.contributor.orcID0000-0001-9274-7467en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergien_US
dc.contributor.researcherIDAGE-3003-2022en_US


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