Short-term Covid-19 forecast for latecomers - Marcelo C. Medeiros; Alexandre Street; Davi Vallad\~ao; Gabriel Vasconcelos; Eduardo Zilberman (Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio), Departamento de Economia, 2021)
The number of Covid-19 cases is increasing dramatically worldwide, with several countries experiencing a second and worse wave. Therefore, the availability of reliable forecasts for the number of cases and deaths 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 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 four different countries: Brazil, Chile, Mexico, and Portugal. We show that the methodology performs very well. These forecasts aim to foster a better short-run management of the health system capacity and can be applied not only to countries but to different regions within a country, as well.
Keywords: 330; Covid-19; LASSO; Forecasting; Pandemics; Coronavirus; Prognoseverfahren; Regressionsanalyse; Kointegration; Sterblichkeit; Theorie; Brasilien