Please use this identifier to cite or link to this item: http://hdl.handle.net/10668/3441
Title: Predicting mortality for Covid-19 in the US using the delayed elasticity method
Authors: Hierro, Luis Ángel
Garzón, Antonio J.
Atienza-Montero, Pedro
Márquez, José Luis
metadata.dc.contributor.authoraffiliation: [Hierro,LA; Garzón,AJ; Atienza-Montero,P] Department of Economics and Economic History, University of Seville, Seville, Spain. [Márquez,JL] University Hospital Virgen del Rocio, Seville, Spain.
Keywords: COVID-19;Forecasting;Health planning;Models, statistical;Humans;Public health;Predicción;Planificación en salud;Modelos estadísticos;Humanos;Salud pública;Mortalidad
metadata.dc.subject.mesh: Medical Subject Headings::Diseases::Virus Diseases::RNA Virus Infections::Nidovirales Infections::Coronaviridae Infections::Coronavirus Infections
Medical Subject Headings::Anthropology, Education, Sociology and Social Phenomena::Social Sciences::Forecasting
Medical Subject Headings::Health Care::Health Care Economics and Organizations::Health Planning
Medical Subject Headings::Organisms::Eukaryota::Animals::Chordata::Vertebrates::Mammals::Primates::Haplorhini::Catarrhini::Hominidae::Humans
Medical Subject Headings::Health Care::Health Care Quality, Access, and Evaluation::Quality of Health Care::Health Care Evaluation Mechanisms::Statistics as Topic::Models, Statistical
Medical Subject Headings::Health Care::Environment and Public Health::Public Health
Issue Date: 30-Nov-2020
Publisher: Springer Nature
Citation: Hierro LA, Garzón AJ, Atienza-Montero P, Márquez JL. Predicting mortality for Covid-19 in the US using the delayed elasticity method. Sci Rep. 2020 Nov 30;10(1):20811.
Abstract: The evolution of the pandemic caused by COVID-19, its high reproductive number and the associated clinical needs, is overwhelming national health systems. We propose a method for predicting the number of deaths, and which will enable the health authorities of the countries involved to plan the resources needed to face the pandemic as many days in advance as possible. We employ OLS to perform the econometric estimation. Using RMSE, MSE, MAPE, and SMAPE forecast performance measures, we select the best lagged predictor of both dependent variables. Our objective is to estimate a leading indicator of clinical needs. Having a forecast model available several days in advance can enable governments to more effectively face the gap between needs and resources triggered by the outbreak and thus reduce the deaths caused by COVID-19.
URI: http://hdl.handle.net/10668/3441
metadata.dc.relation.publisherversion: https://www-nature-com.bvsspa.idm.oclc.org/articles/s41598-020-76490-8#Sec1
metadata.dc.identifier.doi: 10.1038/s41598-020-76490-8
ISSN: 2045-2322
Appears in Collections:01- Artículos - Hospital Virgen del Rocío

Files in This Item:
File Description SizeFormat 
Hierro_PredictingMortalityFor.pdfArtículo publicado1,02 MBAdobe PDFView/Open


This item is protected by original copyright



This item is licensed under a Creative Commons License Creative Commons