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We have studied the dynamic evolution of the Covid-19 pandemic in Argentina.The marked heterogeneity in population density and the very extensive geographyof the country becomes a challenge itself. Standard compartment models failwhen they are implemented in the Argentina case. We extended a previoussuccessful model to describe the geographical spread of the AH1N1 influenzaepidemic of 2009 in two essential ways: we added a stochastic local mobilitymechanism, and we introduced a new compartment in order to take into accountthe isolation of infected asymptomatic detected people. Two fundamentalparameters drive the dynamics: the time elapsed between contagious andisolation of infected individuals ($\alpha$) and the ratio of people isolatedover the total infected ones ($p$). The evolution is more sensitive to the$p-$parameter. The model not only reproduces the real data but also predictsthe second wave before the former vanishes. This effect is intrinsic ofextensive countries with heterogeneous population density and interconnection.The model presented here becomes a good predictor of the effects of publicpolicies as, for instance, the unavoidable vaccination campaigns starting atpresent in the world.

Detecting infected asymptomatic cases in a stochastic model for spread of Covid-19. The case of Argentina