Predicting mortality for Covid-19 in the US. Delayed Elasticity Method

Luis Angel Hierro-Recio, Antonio José Garzón-Gordón, Pedro Atienza-Montero, José Luis Márquez

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Received date: 14th April 2020

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, and through RMSE, MSE, MAPE, and SMAPE forecast performance measures we select the best lagged predictor of both dependent variables.  Our objective is rather 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.

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This is an abstract of a preprint hosted on a preprint server, which is currently undergoing peer review at Scientific Reports. The findings have yet to be thoroughly evaluated, nor has a decision on ultimate publication been made. Therefore, the results reported should not be considered conclusive, and these findings should not be used to inform clinical practice, or public health policy, or be promoted as verified information.


Scientific Reports

Nature Research, Springer Nature