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Artículo
A COVID-19 Recovery Strategy Based on the Health System Capacity Modeling. Implications on Citizen Self-management
dc.creator | Crespo Márquez, Adolfo | es |
dc.creator | SIM Research Group (ETSI. University of Seville) | es |
dc.date.accessioned | 2020-04-17T19:24:08Z | |
dc.date.available | 2020-04-17T19:24:08Z | |
dc.date.issued | 2020-04 | |
dc.identifier.citation | Crespo Márquez, A. y SIM Research Group (ETSI. University of Seville), (2020). A COVID-19 Recovery Strategy Based on the Health System Capacity Modeling. Implications on Citizen Self-management. Computers & Industrial Engineering | |
dc.identifier.issn | 0360-8352 | es |
dc.identifier.uri | https://hdl.handle.net/11441/95407 | |
dc.description | Versión preprint depositada sin articulo publicado dada la actualidad del tema. *Solicitud de los autores | es |
dc.description.abstract | Confinement ends, and recovery phase should be accurate planned. Health System (HS) capacity, specially ICUs and plants capacity and availability, will remain the key stone in this new Covid-19 pandemic life cycle phase. Until massive vaccination programs will be a real option (vaccine developed, world wield production capacity and effective and efficient administration process), date that will mark recovery phase end, important decisions should be taken. Not only by authorities. Citizen self-management and organizations self-management will be crucial. This means: citizen and organizations day a day decision in order to control their own risks (infecting others and being infected). This paper proposes a management tool that is based on a ICUs and plants capacity model. Principal outputs of this tool are, by sequential order and by last best data available: (i) ICUs and plants saturation estimation data (according to incoming rate of patients), (ii) with this results new local and temporal confinement measure can be planned and also a dynamic analysis can be done to estimate maximum Ro saturation scenarios, and finally (iii) provide citizen with clear and accurate data allow them adapting their behavior to authorities’ previous recommendations. One common objective: to accelerate as much as possible socioeconomic normalization with a strict control over HS relapses risk. | es |
dc.format | application/pdf | es |
dc.format.extent | 18 p. | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Computers & Industrial Engineering | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Health system | es |
dc.subject | Capacity planning | es |
dc.subject | Covid-19 recovery | es |
dc.subject | Queue theory | es |
dc.subject | Simulation | es |
dc.subject | System Dynamics | es |
dc.subject | Citizen Self-management | |
dc.title | A COVID-19 Recovery Strategy Based on the Health System Capacity Modeling. Implications on Citizen Self-management | es |
dc.type | info:eu-repo/semantics/article | es |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Organización Industrial y Gestión de Empresas I | es |
dc.contributor.group | Universidad de Sevilla. TEP134: Organizacion Industrial | es |
dc.journaltitle | Computers & Industrial Engineering | es |
Ficheros | Tamaño | Formato | Ver | Descripción |
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A COVID-19 RECOVERY STRATEGY ... | 1.517Mb | [PDF] | Ver/ | Versión Preprint |