Modelling and predicting the growth dynamics of Covid-19 pandemic: A comparative study including Turkey

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dc.authoridErfan Babaee Tirkolaee / 0000-0003-1664-9210en_US
dc.authorscopusidErfan Babaee Tirkolaee / 57196032874
dc.authorwosidErfan Babaee Tirkolaee / U-3676-2017en_US
dc.contributor.authorTirkolaee, Erfan Babaee
dc.contributor.authorAydin, Nadi Serhan
dc.date.accessioned2022-07-07T05:50:18Z
dc.date.available2022-07-07T05:50:18Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Endüstri Mühendisliği Bölümüen_US
dc.description.abstractEstimating the growth dynamics of a pandemic is critical for policy makers to fine-tune emergency policies in health and other public sectors. The paper presents country-level calibration and prediction results on some well-known models in the literature, namely, the logistic, exponential, Gompertz, SIR and SEIR models. The models are implemented on real data from various countries, including Turkey, and their performance for different estimation windows have been analyzed using R2 scores. The computational results are obtained using Python. The Gompertz model outperforms other models by consistently offering a better fit for the total number of infected. The exponential model is helpful in describing the growth dynamics in the early stages of the COVID-19 pandemic. Suspected-Infected-Recovered (SIR) and Susceptible-Exposed-Infectious-Removed (SEIR) models display a fair performance on the underlying active cases data in many circumstances. Quantitative models can offer policy makers in Turkey and elsewhere a better insight on the evolution of pandemic when everything else is held constant and the infections follow a typical path. The results can be highly sensitive to changes in policies. There is not a single model that can perfectly mimic all stages of pandemic. An ensemble model or multi-modal distributions can be used to capture the evolution of multi-wave pandemics.en_US
dc.identifier.citationAydın, N. S. & Tirkolaee, E. B. (2022). MODELLING AND PREDICTING THE GROWTH DYNAMICS OF COVID-19 PANDEMIC: A COMPARATIVE STUDY INCLUDING TURKEY . Journal of Turkish Operations Management , 6 (1) , 943-954 . Retrieved from https://dergipark.org.tr/tr/pub/jtom/issue/70951/980254en_US
dc.identifier.endpage954en_US
dc.identifier.issn2630-6433en_US
dc.identifier.issue1en_US
dc.identifier.startpage943en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2990
dc.identifier.volume6en_US
dc.institutionauthorTirkolaee, Erfan Babaee
dc.language.isoenen_US
dc.publisherDergiParken_US
dc.relation.ispartofJournal of Turkish Operations Managementen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectEpidemics Modellingen_US
dc.subjectExponential Modelen_US
dc.subjectLogistic Modelen_US
dc.subjectGompertz Growthen_US
dc.subjectSIR/SEIR Modelen_US
dc.titleModelling and predicting the growth dynamics of Covid-19 pandemic: A comparative study including Turkeyen_US
dc.typeArticleen_US

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