Cargando…

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

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 prev...

Descripción completa

Detalles Bibliográficos
Autores principales: Barreiro, N. L., Govezensky, T., Bolcatto, P. G., Barrio, R. A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8113491/
https://www.ncbi.nlm.nih.gov/pubmed/33976342
http://dx.doi.org/10.1038/s41598-021-89517-5
_version_ 1783690871537926144
author Barreiro, N. L.
Govezensky, T.
Bolcatto, P. G.
Barrio, R. A.
author_facet Barreiro, N. L.
Govezensky, T.
Bolcatto, P. G.
Barrio, R. A.
author_sort Barreiro, N. L.
collection PubMed
description 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 ([Formula: see text] ) and the ratio of people isolated over the total infected ones (p). The evolution is more sensitive to the [Formula: see text] 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 Online
Article
Text
id pubmed-8113491
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Nature Publishing Group UK
record_format MEDLINE/PubMed
spelling pubmed-81134912021-05-12 Detecting infected asymptomatic cases in a stochastic model for spread of Covid-19: the case of Argentina Barreiro, N. L. Govezensky, T. Bolcatto, P. G. Barrio, R. A. Sci Rep Article 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 ([Formula: see text] ) and the ratio of people isolated over the total infected ones (p). The evolution is more sensitive to the [Formula: see text] 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. Nature Publishing Group UK 2021-05-11 /pmc/articles/PMC8113491/ /pubmed/33976342 http://dx.doi.org/10.1038/s41598-021-89517-5 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Barreiro, N. L.
Govezensky, T.
Bolcatto, P. G.
Barrio, R. A.
Detecting infected asymptomatic cases in a stochastic model for spread of Covid-19: the case of Argentina
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8113491/
https://www.ncbi.nlm.nih.gov/pubmed/33976342
http://dx.doi.org/10.1038/s41598-021-89517-5
work_keys_str_mv AT barreironl detectinginfectedasymptomaticcasesinastochasticmodelforspreadofcovid19thecaseofargentina
AT govezenskyt detectinginfectedasymptomaticcasesinastochasticmodelforspreadofcovid19thecaseofargentina
AT bolcattopg detectinginfectedasymptomaticcasesinastochasticmodelforspreadofcovid19thecaseofargentina
AT barriora detectinginfectedasymptomaticcasesinastochasticmodelforspreadofcovid19thecaseofargentina