COVID-19 modeling based on real geographic and population data

dc.contributor.authorBaysazan, Emir
dc.contributor.authorBerker, A. Nihat
dc.contributor.authorMandal, Hasan
dc.contributor.authorKaygusuz, Hakan
dc.date.accessioned2023-10-19T15:12:54Z
dc.date.available2023-10-19T15:12:54Z
dc.date.issued2023
dc.department-temp[Baysazan, Emir] Istanbul Univ, Council Higher Educ, TEBIP High Performers Program, Istanbul, Turkiye; [Berker, A. Nihat] Kadir Has Univ, Fac Engn & Nat Sci, Istanbul, Turkiye; [Berker, A. Nihat] TUBITAK Res Inst Fundamental Sci, Kocaeli, Turkiye; [Berker, A. Nihat] MIT, Dept Phys, Cambridge, MA USA; [Mandal, Hasan] Sci & Technol Res Council Turkiye TUBITAK, Ankara, Turkiye; [Kaygusuz, Hakan] Altinbas Univ, Fac Engn & Architecture, Dept Basic Sci, Istanbul, Turkiye; [Kaygusuz, Hakan] Sabanci Univ, SUNUM Nanotechnol Res Ctr, Istanbul, Turkiyeen_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 Turkiye 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 Istanbul, the epidemic quickly expands between steps 60 and 100. Simulation results fit the actual mortality data in Turkiye. 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.description.sponsorshipTurkish Academy of Sciences [T?BA]en_US
dc.description.sponsorshipAcknowledgments We thank F. Bedia Erim for a critical reading of our manuscript. A. Nihat Berker gratefully acknowledges support by Turkish Academy of Sciences (T?BA) .en_US
dc.identifier.citation1
dc.identifier.doi10.55730/1300-0144.5589en_US
dc.identifier.endpage339en_US
dc.identifier.issn1300-0144
dc.identifier.issn1303-6165
dc.identifier.issue1en_US
dc.identifier.pmid36945958en_US
dc.identifier.scopus2-s2.0-85149134224en_US
dc.identifier.scopusqualityQ3
dc.identifier.startpage333en_US
dc.identifier.trdizinidhttps://search.trdizin.gov.tr/yayin/detay/1161052en_US
dc.identifier.urihttps://doi.org/10.55730/1300-0144.5589
dc.identifier.uri1161052
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5559
dc.identifier.volume53en_US
dc.identifier.wosWOS:000941667500039en_US
dc.identifier.wosqualityQ4
dc.khas20231019-WoSen_US
dc.language.isoenen_US
dc.publisherTubitak Scientific & Technological Research Council Turkeyen_US
dc.relation.ispartofTurkish Journal of Medical Sciencesen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectMonte Carlo simulationen_US
dc.subjectepidemicen_US
dc.subjectgeographical modelen_US
dc.subjectsusceptible-infected-quarantine-recovered modelen_US
dc.subjectCOVID-19en_US
dc.titleCOVID-19 modeling based on real geographic and population dataen_US
dc.typeArticleen_US
dspace.entity.typePublication

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