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Short-term Covid-19 forecast for latecomers.

Marcelo C Medeiros1, Alexandre Street2, Davi Valladão3

  • 1Department of Economics, Pontifical Catholic University of Rio de Janeiro, Brazil.

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|October 18, 2021
PubMed
Summary

Reliable COVID-19 case and fatality forecasts are crucial. This study introduces a penalized LASSO regression model for short-term predictions in latecomer countries, outperforming other methods.

Keywords:
Covid-19ForecastingInfectious diseasesLASSOPandemics

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Despite vaccination, several countries face significant COVID-19 waves.
  • Accurate short-term forecasting of cases and fatalities is essential for healthcare management.

Purpose of the Study:

  • To develop a reliable statistical method for real-time, short-term forecasting of COVID-19 cases and deaths.
  • To specifically address the needs of 'latecomer' countries in their pandemic response.

Main Methods:

  • Utilized a penalized LASSO regression model incorporating an error correction mechanism.
  • Modeled latecomer countries based on countries at similar pandemic stages previously.
  • Employed an adaptive rolling-window scheme for forecasting.

Main Results:

  • The proposed methodology demonstrated strong performance across 45 countries.
  • Detailed analysis for Brazil, Chile, Mexico, and Portugal confirmed its effectiveness.
  • The model significantly outperformed alternative forecasting methods.

Conclusions:

  • The penalized LASSO regression with error correction provides accurate short-term forecasts for COVID-19.
  • This approach aids in better short-run healthcare system management.
  • The framework is adaptable for regional forecasting and other infectious diseases.