Publication:
A mathematical model for equitable in-country COVID-19 vaccine allocation

dc.contributor.authorKoyuncu, Burcu Balçık
dc.contributor.authorYücesoy, Ecem
dc.contributor.authorAkça, Berna
dc.contributor.authorKarakaya, Sırma
dc.contributor.authorKaplan, Asena Ayse
dc.contributor.authorBaharmand, H.
dc.contributor.authorSgarbossa, F.
dc.contributor.departmentIndustrial Engineering
dc.contributor.ozuauthorKOYUNCU, Burcu Balçık
dc.contributor.ozugradstudentYücesoy, Ecem
dc.contributor.ozugradstudentAkça, Berna
dc.contributor.ozugradstudentKarakaya, Sırma
dc.contributor.ozugradstudentKaplan, Asena Ayse
dc.date.accessioned2023-08-14T05:59:04Z
dc.date.available2023-08-14T05:59:04Z
dc.date.issued2022
dc.description.abstractGiven the scarcity of COVID-19 vaccines, equitable (fair) allocation of limited vaccines across the main administrative units of a country (e.g. municipalities) has been an important concern for public health authorities worldwide. In this study, we address the equitable allocation of the COVID-19 vaccines inside countries by developing a novel, evidence-based mathematical model that accounts for multiple priority groups (e.g. elderly, healthcare workers), multiple vaccine types, and regional characteristics (e.g. storage capacities, infection risk levels). Our research contributes to the literature by developing and validating a model that proposes equitable vaccine allocation alternatives in a very short time by (a) minimising deviations from the so-called ‘fair coverage’ levels that are computed based on weighted pro-rata rations, and (b) imposing minimum coverage thresholds to control the allocation of vaccines to higher priority groups and regions. To describe the merits of our model, we provide several equity and effectiveness metrics, and present insights on different allocation policies. We compare our methodology with similar models in the literature and show its better performance in achieving equity. To illustrate the performance of our model in practice, we perform a comprehensive numerical study based on actual data corresponding to the early vaccination period in Turkey.
dc.description.sponsorshipNorges Forskningsråd
dc.description.versionPublisher version
dc.identifier.doi10.1080/00207543.2022.2110014
dc.identifier.endpage7526
dc.identifier.issn0020-7543
dc.identifier.issue24
dc.identifier.scopus2-s2.0-85136525626
dc.identifier.startpage7502
dc.identifier.urihttp://hdl.handle.net/10679/8650
dc.identifier.urihttps://doi.org/10.1080/00207543.2022.2110014
dc.identifier.volume60
dc.identifier.wos000842837000001
dc.language.isoeng
dc.peerreviewedyes
dc.publicationstatusPublished
dc.publisherTaylor and Francis
dc.relation.ispartofInternational Journal of Production Research
dc.relation.publicationcategoryInternational Refereed Journal
dc.rightsAttribution 4.0 International
dc.rightsopenAccess
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subject.keywordsCase study
dc.subject.keywordsCOVID-19
dc.subject.keywordsEquity
dc.subject.keywordsInteger programming
dc.subject.keywordsVaccine allocation
dc.titleA mathematical model for equitable in-country COVID-19 vaccine allocation
dc.typearticle
dspace.entity.typePublication
relation.isOrgUnitOfPublication5dd73c02-fd2d-43e0-9a23-71bab9ae0b6b
relation.isOrgUnitOfPublication.latestForDiscovery5dd73c02-fd2d-43e0-9a23-71bab9ae0b6b

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