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...
Autores principales: | , , , |
---|---|
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 |