An extended robust mathematical model to project the course of COVID-19 epidemic in Iran

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dc.authoridErfan Babaee Tirkolaee / 0000-0003-1664-9210
dc.authorscopusidErfan Babaee Tirkolaee / 57196032874
dc.authorwosidErfan Babaee Tirkolaee / U-3676-2017
dc.contributor.authorLotf, Reza
dc.contributor.authorKheiri, Kiana
dc.contributor.authorSadeghi, Ali
dc.contributor.authorTirkolaee, Erfan Babaee
dc.date.accessioned2022-01-18T08:43:34Z
dc.date.available2022-01-18T08:43:34Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractThis research develops a regression-based Robust Optimization (RO) approach to efficiently predict the number of patients with confirmed infection caused by the recent Coronavirus Disease (COVID-19). The main idea is to study the dynamics of the COVID-19 outbreak at the first stage and then provide efficient insights to estimate the necessary resources accordingly. The convex RO with Mean Absolute Deviation (MAD) objective function is utilized to project the course of COVID-19 epidemic in Iran. To validate the performance of the suggested model, a real-case study is investigated and compared to several well-known forecasting models including Simple Moving Average, Exponential Moving Average, Weighted Moving Average and Exponential Smoothing with Trend Adjustment models. Furthermore, the effect of parameter uncertainties is examined using a set of sensitivity analyses. The results demonstrate that by increasing the degree (coefficient) of regression up to 8, MAD value decreases to 1378.12, and consequently, the corresponding equation becomes more accurate. On the other hand, from the 8th degree onwards, MAD value follows an upward trend. Furthermore, by increasing the level of regression uncertainty, MAD value follows a downward trend to reach 1309.28 and the estimation accuracy of the model increases accordingly. Finally, our proposed model achieves the least MAD and the greatest correlation coefficient against the other models.en_US
dc.identifier.citationLotfi, R., Kheiri, K., Sadeghi, A., & Babaee Tirkolaee, E. (2022). An extended robust mathematical model to project the course of COVID-19 epidemic in iran. Annals of Operations Researchen_US
dc.identifier.doi10.1007/s10479-021-04490-6en_US
dc.identifier.issn0254-5330en_US
dc.identifier.pmid35013634en_US
dc.identifier.scopus2-s2.0-85122341214en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://10.1007/s10479-021-04490-6
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2390
dc.identifier.wosWOS:000739812100001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorTirkolaee, Erfan Babaee
dc.language.isoenen_US
dc.publisherSPRINGERen_US
dc.relation.ispartofAnnals of Operations Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCOVID-19 pandemicen_US
dc.subjectPredictionen_US
dc.subjectRegressionen_US
dc.subjectRobust optimizationen_US
dc.subjectMean absolute deviationen_US
dc.titleAn extended robust mathematical model to project the course of COVID-19 epidemic in Iranen_US
dc.typeArticleen_US

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