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 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 extend...

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Autores principales:	N. L. Barreiro, T. Govezensky, P. G. Bolcatto, R. A. Barrio
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c0e72ecda32b4aafa95b9e6af79746cf
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spelling	oai:doaj.org-article:c0e72ecda32b4aafa95b9e6af79746cf2021-12-02T16:57:57ZDetecting infected asymptomatic cases in a stochastic model for spread of Covid-19: the case of Argentina10.1038/s41598-021-89517-52045-2322https://doaj.org/article/c0e72ecda32b4aafa95b9e6af79746cf2021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-89517-5https://doaj.org/toc/2045-2322Abstract 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 ( $$\alpha$$ α ) and the ratio of people isolated over the total infected ones (p). The evolution is more sensitive to the $$p-$$ 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 has proven to be a reliable predictor of the effects of public policies as, for instance, the unavoidable vaccination campaigns starting at present in the world an particularly in Argentina.N. L. BarreiroT. GovezenskyP. G. BolcattoR. A. BarrioNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q N. L. Barreiro T. Govezensky P. G. Bolcatto R. A. Barrio Detecting infected asymptomatic cases in a stochastic model for spread of Covid-19: the case of Argentina
description	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 ( $$\alpha$$ α ) and the ratio of people isolated over the total infected ones (p). The evolution is more sensitive to the $$p-$$ 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 has proven to be a reliable predictor of the effects of public policies as, for instance, the unavoidable vaccination campaigns starting at present in the world an particularly in Argentina.
format	article
author	N. L. Barreiro T. Govezensky P. G. Bolcatto R. A. Barrio
author_facet	N. L. Barreiro T. Govezensky P. G. Bolcatto R. A. Barrio
author_sort	N. L. Barreiro
title	Detecting infected asymptomatic cases in a stochastic model for spread of Covid-19: the case of Argentina
title_short	Detecting infected asymptomatic cases in a stochastic model for spread of Covid-19: the case of Argentina
title_full	Detecting infected asymptomatic cases in a stochastic model for spread of Covid-19: the case of Argentina
title_fullStr	Detecting infected asymptomatic cases in a stochastic model for spread of Covid-19: the case of Argentina
title_full_unstemmed	Detecting infected asymptomatic cases in a stochastic model for spread of Covid-19: the case of Argentina
title_sort	detecting infected asymptomatic cases in a stochastic model for spread of covid-19: the case of argentina
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c0e72ecda32b4aafa95b9e6af79746cf
work_keys_str_mv	AT nlbarreiro detectinginfectedasymptomaticcasesinastochasticmodelforspreadofcovid19thecaseofargentina AT tgovezensky detectinginfectedasymptomaticcasesinastochasticmodelforspreadofcovid19thecaseofargentina AT pgbolcatto detectinginfectedasymptomaticcasesinastochasticmodelforspreadofcovid19thecaseofargentina AT rabarrio detectinginfectedasymptomaticcasesinastochasticmodelforspreadofcovid19thecaseofargentina
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Detecting infected asymptomatic cases in a stochastic model for spread of Covid-19: the case of Argentina

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