  This paper extends the canonical model of epidemiology, SIRD model, to allow
for time varying parameters for real-time measurement of the stance of the
COVID-19 pandemic. Time variation in model parameters is captured using the
generalized autoregressive score modelling structure designed for the typically
daily count data related to pandemic. The resulting specification permits a
flexible yet parsimonious model structure with a very low computational cost.
This is especially crucial at the onset of the pandemic when the data is scarce
and the uncertainty is abundant. Full sample results show that countries
including US, Brazil and Russia are still not able to contain the pandemic with
the US having the worst performance. Furthermore, Iran and South Korea are
likely to experience the second wave of the pandemic. A real-time exercise show
that the proposed structure delivers timely and precise information on the
current stance of the pandemic ahead of the competitors that use rolling
window. This, in turn, transforms into accurate short-term predictions of the
active cases. We further modify the model to allow for unreported cases.
Results suggest that the effects of the presence of these cases on the
estimation results diminish towards the end of sample with the increasing
number of testing.
