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Short-Term Covid-19 Forecast for Latecomers

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Listed:
  • Marcelo Medeiros
  • Alexandre Street
  • Davi Vallad~ao
  • Gabriel Vasconcelos
  • Eduardo Zilberman

Abstract

The number of Covid-19 cases is increasing dramatically worldwide. Therefore, the availability of reliable forecasts for the number of cases in the coming days is of fundamental importance. We propose a simple statistical method for short-term real-time forecasting of the number of Covid-19 cases and fatalities in countries that are latecomers -- i.e., countries where cases of the disease started to appear some time after others. In particular, we propose a penalized (LASSO) regression with an error correction mechanism to construct a model of a latecomer in terms of the other countries that were at a similar stage of the pandemic some days before. By tracking the number of cases and deaths in those countries, we forecast through an adaptive rolling-window scheme the number of cases and deaths in the latecomer. We apply this methodology to Brazil, and show that (so far) it has been performing very well. These forecasts aim to foster a better short-run management of the health system capacity.

Suggested Citation

  • Marcelo Medeiros & Alexandre Street & Davi Vallad~ao & Gabriel Vasconcelos & Eduardo Zilberman, 2020. "Short-Term Covid-19 Forecast for Latecomers," Papers 2004.07977, arXiv.org, revised Sep 2021.
    • Handle: RePEc:arx:papers:2004.07977
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    1. Miljana Milić & Jelena Milojković & Miljan Jeremić, 2022. "Optimal Neural Network Model for Short-Term Prediction of Confirmed Cases in the COVID-19 Pandemic," Mathematics, MDPI, vol. 10(20), pages 1-18, October.

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