Improving input parameter estimation in online pandemic simulation

García de Vicuña Bilbao, Daniel; Mallor Giménez, Fermín

Publication:
Improving input parameter estimation in online pandemic simulation

dc.contributor.author	García de Vicuña Bilbao, Daniel
dc.contributor.author	Mallor Giménez, Fermín
dc.contributor.department	Estatistika, Informatika eta Matematika	eu
dc.contributor.department	Institute of Smart Cities - ISC	en
dc.contributor.department	Estadística, Informática y Matemáticas	es_ES
dc.date.accessioned	2022-10-24T11:37:30Z
dc.date.available	2023-02-23T00:00:16Z
dc.date.issued	2021
dc.date.updated	2022-10-24T11:37:30Z
dc.description.abstract	Simulation 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.sponsorship	This 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.embargo.lift	2023-02-23
dc.embargo.terms	2023-02-23
dc.format.mimetype	application/pdf	en
dc.identifier.citation	Garcia-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/WSC52266.2021.9715311	en
dc.identifier.doi	10.1109/WSC52266.2021.9715311
dc.identifier.isbn	978-1-6654-3311-2
dc.identifier.uri	https://academica-e.unavarra.es/handle/2454/44250
dc.language.iso	eng	en
dc.publisher	IEEE	en
dc.relation.ispartof	2021 Winter Simulation Conference (WSC), 2021, pp. 1-12	en
dc.relation.projectID	info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-114031RB-I00/ES/	en
dc.relation.publisherversion	https://doi.org/10.1109/WSC52266.2021.9715311
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.rights.accessRights	info:eu-repo/semantics/openAccess	en
dc.rights.accessRights	Acceso Embargado	es_ES
dc.subject	Data handling	en
dc.subject	Expectation-maximisation algorithm	en
dc.subject	Health care	en
dc.subject	Hospitals	en
dc.subject	Internet	en
dc.subject	Parameter estimation	en
dc.title	Improving input parameter estimation in online pandemic simulation	en
dc.type	Contribución a congreso / Biltzarrerako ekarpena	es
dc.type	info:eu-repo/semantics/conferenceObject	en
dc.type.version	Versión publicada / Argitaratu den bertsioa	es
dc.type.version	info:eu-repo/semantics/publishedVersion	en
dspace.entity.type	Publication
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