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

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