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Authordc.contributor.authorGoic Figueroa, Marcel 
Authordc.contributor.authorBozanic Leal, Mirko Slovan 
Authordc.contributor.authorBadal, Magdalena 
Authordc.contributor.authorBasso Sotz, Leonardo 
Cita de ítemdc.identifier.citationPLoS ONE 16(1): e0245272 - 2021es_ES
Abstractdc.description.abstractBy early May 2020, the number of new COVID-19 infections started to increase rapidly in Chile, threatening the ability of health services to accommodate all incoming cases. Suddenly, ICU capacity planning became a first-order concern, and the health authorities were in urgent need of tools to estimate the demand for urgent care associated with the pandemic. In this article, we describe the approach we followed to provide such demand forecasts, and we show how the use of analytics can provide relevant support for decision making, even with incomplete data and without enough time to fully explore the numerical properties of all available forecasting methods. The solution combines autoregressive, machine learning and epidemiological models to provide a short-term forecast of ICU utilization at the regional level. These forecasts were made publicly available and were actively used to support capacity planning. Our predictions achieved average forecasting errors of 4% and 9% for one- and two-week horizons, respectively, outperforming several other competing forecasting models.es_ES
Patrocinadordc.description.sponsorshipInstituto Sistemas Complejos de Ingeniería, ISCI ANID PIA AFB180003 Instituto Milenio para la investigación de imperfecciones de mercado y políticas públicas IS130002es_ES
Publisherdc.publisherPublic Library Sciencees_ES
Type of licensedc.rightsAttribution-NonCommercial-NoDerivs 3.0 Chile*
Link to Licensedc.rights.uri*
Sourcedc.sourcePlos Onees_ES
Títulodc.titleCOVID-19: short-term forecast of ICU beds in times of crisises_ES
Document typedc.typeArtículo de revistaes_ES
Indexationuchile.indexArtículo de publicación ISIes_ES

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Attribution-NonCommercial-NoDerivs 3.0 Chile
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Chile