  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.
