RT Journal Article
T1 Predicting mortality for Covid-19 in the US using the delayed elasticity method
A1 Hierro, Luis Ángel
A1 Garzón, Antonio J.
A1 Atienza-Montero, Pedro
A1 Márquez, José Luis
K1 COVID-19
K1 Forecasting
K1 Health planning
K1 Models, statistical
K1 Humans
K1 Public health
K1 Predicción
K1 Planificación en salud
K1 Modelos estadísticos
K1 Humanos
K1 Salud pública
K1 Mortalidad
AB 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.
PB Springer Nature
YR 2020
FD 2020-11-30
LK http://hdl.handle.net/10668/3441
UL http://hdl.handle.net/10668/3441
LA en
NO 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.
DS RISalud
RD Apr 11, 2025