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

TL;DR

This study models the spread of Covid-19 in Argentina, incorporating stochastic local mobility and asymptomatic case isolation, to better predict epidemic waves and assess public policy impacts.

Contribution

It extends a previous epidemic model by adding stochastic mobility and asymptomatic isolation compartments, improving prediction accuracy for heterogeneous populations.

Findings

The model accurately reproduces real Covid-19 data in Argentina.

It predicts a second wave before the first subsides.

The model is sensitive to the isolation ratio parameter p.

Abstract

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.