COVID-19 modeling based on real geographic and population data

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dc.contributor.authorBaysazan, Emir
dc.contributor.authorBerker, A. Nihat
dc.contributor.authorMandal, Hasan
dc.contributor.authorKaygusuz, Hakan
dc.date.accessioned2023-03-16T08:37:16Z
dc.date.available2023-03-16T08:37:16Z
dc.date.issued2023en_US
dc.departmentFakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Temel Bilimler Bölümüen_US
dc.description.abstractBackground/aim: Intercity travel is one of the most important parameters for combating a pandemic. The ongoing COVID-19 pandemic has resulted in different computational studies involving intercity connections. In this study, the effects of intercity connections during an epidemic such as COVID-19 are evaluated using a new network model. Materials and methods: This model considers the actual geographic neighborhood and population density data. This new model is applied to actual Turkish data by means of provincial connections and populations. A Monte Carlo algorithm with a hybrid lattice model is applied to a lattice with 8802 data points. Results: Around Monte Carlo step 70, the number of active cases in Türkiye reaches up to 8.0% of the total population, which is followed by a second wave at around Monte Carlo step 100. The number of active cases vanishes around Monte Carlo step 160. Starting with İstanbul, the epidemic quickly expands between steps 60 and 100. Simulation results fit the actual mortality data in Türkiye. Conclusion: This model is quantitatively very efficient in modeling real-world COVID-19 epidemic data based on populations and geographical intercity connections, by means of estimating the number of deaths, disease spread, and epidemic termination.en_US
dc.identifier.citationBaysazan, E., Berker, A. N., Mandal, H., & Kaygusuz, H. (2023). COVID-19 modeling based on real geographic and population data. Turkish Journal of Medical Sciences, 53(1), 333-339.en_US
dc.identifier.endpage339en_US
dc.identifier.issn1300-0144
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85149134224
dc.identifier.scopusqualityQ1
dc.identifier.startpage333en_US
dc.identifier.trdizinid1161052
dc.identifier.urihttps://hdl.handle.net/20.500.12939/3442
dc.identifier.volume53en_US
dc.identifier.wosWOS:000941667500039
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.indekslendigikaynakPubMed
dc.institutionauthorKaygusuz, Hakan
dc.language.isoen
dc.publisherTurkiye Kliniklerien_US
dc.relation.ispartofTurkish Journal of Medical Sciences
dc.relation.isversionof10.55730/1300-0144.5589en_US
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectCOVID-19en_US
dc.subjectEpidemicen_US
dc.subjectGeographical Modelen_US
dc.subjectMonte Carlo Simulationen_US
dc.subjectSusceptible-Infected-Quarantine-Recovered Modelen_US
dc.titleCOVID-19 modeling based on real geographic and population data
dc.typeArticle

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