  The continuously growing number of COVID-19 cases pressures healthcare
services worldwide. Accurate short-term forecasting is thus vital to support
country-level policy making. The strategies adopted by countries to combat the
pandemic vary, generating different uncertainty levels about the actual number
of cases. Accounting for the hierarchical structure of the data and
accommodating extra-variability is therefore fundamental. We introduce a new
modelling framework to describe the course of the pandemic with great accuracy,
and provide short-term daily forecasts for every country in the world. We show
that our model generates highly accurate forecasts up to six days ahead, and
use estimated model components to cluster countries based on recent events. We
introduce statistical novelty in terms of modelling the autoregressive
parameter as a function of time, increasing predictive power and flexibility to
adapt to each country. Our model can also be used to forecast the number of
deaths, study the effects of covariates (such as lockdown policies), and
generate forecasts for smaller regions within countries. Consequently, it has
strong implications for global planning and decision making. We constantly
update forecasts and make all results freely available to any country in the
world through an online Shiny dashboard.
