MODELLING AND PREDICTING THE GROWTH DYNAMICS OF COVID-19 PANDEMIC: A COMPARATIVE STUDY INCLUDING TURKEY

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dc.contributor.authorAydın, Nadi Serhan
dc.contributor.authorTirkolaee, Erfan Babaee
dc.date.accessioned2024-05-19T14:23:46Z
dc.date.available2024-05-19T14:23:46Z
dc.date.issued2022
dc.departmentİstinye Üniversitesien_US
dc.description.abstractAbstract Estimating 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 R^2 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. SIR and 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.endpage954en_US
dc.identifier.issn2630-6433
dc.identifier.issue1en_US
dc.identifier.startpage943en_US
dc.identifier.trdizinid1149279en_US
dc.identifier.urihttps://search.trdizin.gov.tr/yayin/detay/1149279
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4117
dc.identifier.volume6en_US
dc.indekslendigikaynakTR-Dizinen_US
dc.language.isoenen_US
dc.relation.ispartofJournal of the Turkish Operations Management (JTOM)en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_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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