  We have studied the dynamic evolution of the Covid-19 pandemic in Argentina.
The marked heterogeneity in population density and the very extensive geography
of the country becomes a challenge itself. Standard compartment models fail
when they are implemented in the Argentina case. We extended a previous
successful model to describe the geographical spread of the AH1N1 influenza
epidemic of 2009 in two essential ways: we added a stochastic local mobility
mechanism, and we introduced a new compartment in order to take into account
the isolation of infected asymptomatic detected people. Two fundamental
parameters drive the dynamics: the time elapsed between contagious and
isolation of infected individuals ($\alpha$) and the ratio of people isolated
over the total infected ones ($p$). The evolution is more sensitive to the
$p-$parameter. The model not only reproduces the real data but also predicts
the second wave before the former vanishes. This effect is intrinsic of
extensive countries with heterogeneous population density and interconnection.
The model presented here becomes a good predictor of the effects of public
policies as, for instance, the unavoidable vaccination campaigns starting at
present in the world.
