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.
Highlights
The evolution of the pandemic caused by COVID-19, its high reproductive number and the associated clinical needs, is overwhelming national health systems
The first type, predictive models, are built for the sole purpose of predicting the evolution of the variable under study using past information from the same variable and employing probabilistic equations[6], exponential smoothing methods[7] or ARIMA techniques[7,8,9]. The latter, which are epidemiological models in the strict sense, are models which explain the spread of the disease, with most of them being of the “compartments” type, and which were developed following the works of Kermack and McKendrick[10,11,12]
The Delayed Elasticity Method (DEM), which we applied to the initial stage of the Covid[19] pandemic in the US, is a new type of model in which we use the relationship between the death variable and the infected cases variable to forecast deaths from Covid-19
Summary
The evolution of the pandemic caused by COVID-19, its high reproductive number and the associated clinical needs, is overwhelming national health systems. The first type, predictive models, are built for the sole purpose of predicting the evolution of the variable under study (number of infections, deaths...) using past information from the same variable and employing probabilistic equations[6], exponential smoothing methods[7] or ARIMA techniques[7,8,9] The latter, which are epidemiological models in the strict sense, are models which explain the spread of the disease, with most of them being of the “compartments” type, and which were developed following the works of Kermack and McKendrick[10,11,12]. Its advantages are that it needs relatively short time series, it defines a prediction window that other models do not define, added to which its predictive accuracy is very high
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