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

Pablo Pincheira-Brown, Andrea Bentancor

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

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.

Original languageEnglish
Article number100486
JournalEpidemics
Volume37
DOIs
StatePublished - Dec 2021
Externally publishedYes

Keywords

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

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