Elsevier

Epidemics

Volume 37, December 2021, 100486
Epidemics

Forecasting COVID-19 infections with the semi-unrestricted Generalized Growth Model

https://doi.org/10.1016/j.epidem.2021.100486Get rights and content
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Highlights

  • We propose a new phenomenological model to forecast COVID-19 infections.

  • This new model is called Semi-Unrestricted Generalized Growth Model (SUGGM).

  • Our model allows for the damping parameter ”p” of the GGM to take negative values.

  • Our SUGGM outperforms forecasts from the LGM, and often from the GGM.

  • We improve forecasts for countries in middle stages of COVID propagation.

Abstract

Recently, the Generalized Growth Model (GGM) has played a prominent role as an effective tool to predict the spread of pandemics exhibiting subexponential growth. A key feature of this model is a damping parameter p that is bounded to the [0,1] interval. By allowing this parameter to take negative values, we show that the GGM can also be useful to predict the spread of COVID-19 in countries that are at middle stages of the pandemic. Using both in-sample and out-of-sample evaluations, we show that a semi-unrestricted version of the model outperforms the traditional GGM in a number of countries when predicting the number of infected people at short horizons. Reductions in Root Mean Squared Prediction Errors (RMSPE) are shown to be substantial. Our results indicate that our semi-unrestricted version of the GGM should be added to the traditional set of phenomenological models used to generate forecasts during early to middle stages of epidemic outbreaks.

Keywords

SARS-CoV-2
Coronavirus disease
COVID-19
Forecasting
Out-of-sample comparison
Growth model
Phenomenological models
Generalized Growth Model

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