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dc.creatorGarcía de Vicuña Bilbao, Danieles_ES
dc.creatorMallor Giménez, Fermínes_ES
dc.date.accessioned2022-10-24T11:37:30Z
dc.date.available2023-02-23T00:00:16Z
dc.date.issued2021
dc.identifier.citationGarcia-Vicuna, D., & Mallor, F. (2021). Improving input parameter estimation in online pandemic simulation. 2021 Winter Simulation Conference (WSC), 1-12. https://doi.org/10.1109/WSC52266.2021.9715311en
dc.identifier.isbn978-1-6654-3311-2
dc.identifier.urihttps://hdl.handle.net/2454/44250
dc.description.abstractSimulation models are suitable tools to represent the complexity and randomness of hospital systems. To be used as forecasting tools during pandemic waves, it is necessary an accurate estimation, by using real-time data, of all input parameters that define the patient pathway and length of stay in the hospital. We propose an estimation method based on an expectation-maximization algorithm that uses data from all patients admitted to the hospital to date. By simulating different pandemic waves, the performance of this method is compared with other two statistical estimators that use only complete data. Results collected to measure the accuracy in the parameters estimation and its influence in the forecasting of necessary resources to provide healthcare to pandemic patients show the better performance of the new estimation method. We also propose a new parameterization of the Gompertz growth model that eases the creation of patient arrival scenarios in the pandemic simulation. © 2021 IEEE.en
dc.description.sponsorshipThis paper was supported by the COVID grant of Navarre's Government 0011-3597-2020-000003 and the grant PID2020-114031RB-I00 (AEI, FEDER EU).en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherIEEEen
dc.relation.ispartof2021 Winter Simulation Conference (WSC), 2021, pp. 1-12en
dc.rights© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work.en
dc.subjectData handlingen
dc.subjectExpectation-maximisation algorithmen
dc.subjectHealth careen
dc.subjectHospitalsen
dc.subjectInterneten
dc.subjectParameter estimationen
dc.titleImproving input parameter estimation in online pandemic simulationen
dc.typeContribución a congreso / Biltzarrerako ekarpenaes
dc.typeinfo:eu-repo/semantics/conferenceObjecten
dc.date.updated2022-10-24T11:37:30Z
dc.contributor.departmentInstitute of Smart Cities - ISCes_ES
dc.contributor.departmentEstadística, Informática y Matemáticases_ES
dc.contributor.departmentEstatistika, Informatika eta Matematikaeu
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.rights.accessRightsAcceso Embargadoes_ES
dc.embargo.terms2023-02-23
dc.identifier.doi10.1109/WSC52266.2021.9715311
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-114031RB-I00/ES/en
dc.relation.publisherversionhttps://doi.org/10.1109/WSC52266.2021.9715311
dc.type.versionVersión publicada / Argitaratu den bertsioaes
dc.type.versioninfo:eu-repo/semantics/publishedVersionen


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