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Predicting COVID-19 in very large countries: The case of Brazil

This work presents a practical proposal for estimating health system utilization for COVID-19 cases. The novel methodology developed is based on the dynamic model known as Susceptible, Infected, Removed and Dead (SIRD). The model was modified to focus on the healthcare system dynamics, rather than m...

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Detalles Bibliográficos
Autores principales: Parro, V. C., Lafetá, M. L. M., Pait, F., Ipólito, F. B., Toporcov, T. N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8248665/
https://www.ncbi.nlm.nih.gov/pubmed/34197489
http://dx.doi.org/10.1371/journal.pone.0253146
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author Parro, V. C.
Lafetá, M. L. M.
Pait, F.
Ipólito, F. B.
Toporcov, T. N.
author_facet Parro, V. C.
Lafetá, M. L. M.
Pait, F.
Ipólito, F. B.
Toporcov, T. N.
author_sort Parro, V. C.
collection PubMed
description This work presents a practical proposal for estimating health system utilization for COVID-19 cases. The novel methodology developed is based on the dynamic model known as Susceptible, Infected, Removed and Dead (SIRD). The model was modified to focus on the healthcare system dynamics, rather than modeling all cases of the disease. It was tuned using data available for each Brazilian state and updated with daily figures. A figure of merit that assesses the quality of the model fit to the data was defined and used to optimize the free parameters. The parameters of an epidemiological model for the whole of Brazil, comprising a linear combination of the models for each state, were estimated considering the data available for the 26 Brazilian states. The model was validated, and strong adherence was demonstrated in most cases.
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spelling pubmed-82486652021-07-09 Predicting COVID-19 in very large countries: The case of Brazil Parro, V. C. Lafetá, M. L. M. Pait, F. Ipólito, F. B. Toporcov, T. N. PLoS One Research Article This work presents a practical proposal for estimating health system utilization for COVID-19 cases. The novel methodology developed is based on the dynamic model known as Susceptible, Infected, Removed and Dead (SIRD). The model was modified to focus on the healthcare system dynamics, rather than modeling all cases of the disease. It was tuned using data available for each Brazilian state and updated with daily figures. A figure of merit that assesses the quality of the model fit to the data was defined and used to optimize the free parameters. The parameters of an epidemiological model for the whole of Brazil, comprising a linear combination of the models for each state, were estimated considering the data available for the 26 Brazilian states. The model was validated, and strong adherence was demonstrated in most cases. Public Library of Science 2021-07-01 /pmc/articles/PMC8248665/ /pubmed/34197489 http://dx.doi.org/10.1371/journal.pone.0253146 Text en © 2021 Parro et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Parro, V. C.
Lafetá, M. L. M.
Pait, F.
Ipólito, F. B.
Toporcov, T. N.
Predicting COVID-19 in very large countries: The case of Brazil
title Predicting COVID-19 in very large countries: The case of Brazil
title_full Predicting COVID-19 in very large countries: The case of Brazil
title_fullStr Predicting COVID-19 in very large countries: The case of Brazil
title_full_unstemmed Predicting COVID-19 in very large countries: The case of Brazil
title_short Predicting COVID-19 in very large countries: The case of Brazil
title_sort predicting covid-19 in very large countries: the case of brazil
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8248665/
https://www.ncbi.nlm.nih.gov/pubmed/34197489
http://dx.doi.org/10.1371/journal.pone.0253146
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