Analysis of Covid 19 Disease With Sir Model and Taylor Matrix Method

dc.contributor.author Uçar, Deniz
dc.contributor.author Çelik, Elçin
dc.date.accessioned 2022-08-03T11:02:29Z
dc.date.available 2022-08-03T11:02:29Z
dc.date.issued 2022
dc.description.abstract Covid 19 emerged in Wuhan, China in December 2019 has continued to spread by affecting the whole world. The pandemic has affected over 328 million people with more than 5 million deaths in over 200 countries which has severely disrupted the healthcare system and halted economies of the countries. The aim of this study is to discuss the numerical solution of the SIR model on the spread of Covid 19 by the Taylor matrix and collocation method for Turkey. Predicting COVID-19 through appropriate models can help us to understand the potential spread in the population so that appropriate action can be taken to prevent further transmission and prepare health systems for medical management of the disease. We deal with Susceptible–Infected–Recovered (SIR) model. One of the proposed model’s improvements is to reflect the societal feedback on the disease and confinement features. We obtain the time dependent rate of transmission of the disease from susceptible β(t) and the rate of recovery from infectious to recovered γ using Turkey epidemic data. We apply the Taylor matrix and collocation method to the SIR model with γ, β(t) and Covid 19 data of Turkey from the date of the first case March 11, 2020 through July 3, 2021. Using this method, we focus on the evolution of the Covid 19 in Turkey. We also show the estimates with the help of graphics and Maple. en_US
dc.identifier.doi 10.3934/math.2022626
dc.identifier.issn 2473-6988 en_US
dc.identifier.issn 2473-6988
dc.identifier.scopus 2-s2.0-85128158662
dc.identifier.uri https://doi.org/10.3934/math.2022626
dc.identifier.uri https://hdl.handle.net/11147/12252
dc.language.iso en en_US
dc.publisher American Institute of Mathematical Sciences en_US
dc.relation.ispartof AIMS Mathematics en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Collocation points en_US
dc.subject COVID-19 en_US
dc.subject Nonlinear differential equation en_US
dc.subject Taylor polynomials en_US
dc.title Analysis of Covid 19 Disease With Sir Model and Taylor Matrix Method en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.author.id 0000-0003-4145-6074
gdc.author.id 0000-0003-4145-6074 en_US
gdc.author.institutional Çelik, Elçin
gdc.bip.impulseclass C4
gdc.bip.influenceclass C5
gdc.bip.popularityclass C4
gdc.coar.access open access
gdc.coar.type text::journal::journal article
gdc.collaboration.industrial false
gdc.contributor.affiliation Uşak Üniversitesi en_US
gdc.contributor.affiliation 01. Izmir Institute of Technology en_US
gdc.description.department İzmir Institute of Technology. Mathematics en_US
gdc.description.endpage 11200 en_US
gdc.description.issue 6 en_US
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality Q1
gdc.description.startpage 11188 en_US
gdc.description.volume 7 en_US
gdc.description.wosquality Q1
gdc.identifier.openalex W4226422359
gdc.identifier.wos WOS:000788060600003
gdc.index.type WoS
gdc.index.type Scopus
gdc.oaire.accesstype GOLD
gdc.oaire.diamondjournal false
gdc.oaire.impulse 8.0
gdc.oaire.influence 3.014511E-9
gdc.oaire.isgreen false
gdc.oaire.keywords taylor polynomials and series
gdc.oaire.keywords covid 19
gdc.oaire.keywords nonlinear differential equation systems
gdc.oaire.keywords QA1-939
gdc.oaire.keywords collocation points
gdc.oaire.keywords sir model
gdc.oaire.keywords Mathematics
gdc.oaire.popularity 8.781733E-9
gdc.oaire.publicfunded false
gdc.oaire.sciencefields 0103 physical sciences
gdc.oaire.sciencefields 0101 mathematics
gdc.oaire.sciencefields 01 natural sciences
gdc.openalex.collaboration National
gdc.openalex.fwci 2.0920973
gdc.openalex.normalizedpercentile 0.81
gdc.opencitations.count 6
gdc.plumx.mendeley 6
gdc.plumx.scopuscites 9
gdc.scopus.citedcount 9
gdc.wos.citedcount 8
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relation.isOrgUnitOfPublication.latestForDiscovery 9af2b05f-28ac-4003-8abe-a4dfe192da5e

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