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Estimative of real number of infections by COVID-19 in Brazil and possible scenarios

This paper attempts to provide methods to estimate the real scenario of the novel coronavirus pandemic in Brazil, specifically in the states of Sao Paulo, Pernambuco, Espirito Santo, Amazonas and the Federal District. By the use of a SEIRD mathematical model with age division, we predict the infecti...

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Detalles Bibliográficos
Autores principales: Cintra, H.P.C., Fontinele, F.N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: KeAi Publishing 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513932/
https://www.ncbi.nlm.nih.gov/pubmed/32995682
http://dx.doi.org/10.1016/j.idm.2020.09.004
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author Cintra, H.P.C.
Fontinele, F.N.
author_facet Cintra, H.P.C.
Fontinele, F.N.
author_sort Cintra, H.P.C.
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description This paper attempts to provide methods to estimate the real scenario of the novel coronavirus pandemic in Brazil, specifically in the states of Sao Paulo, Pernambuco, Espirito Santo, Amazonas and the Federal District. By the use of a SEIRD mathematical model with age division, we predict the infection and death curves, stating the peak date for Brazil and above states. We also carry out a prediction for the ICU demand in these states and for how severe possible collapse in the local health system would be. Finally, we establish some future scenarios including the relaxation on social isolation and the introduction of vaccines and other efficient therapeutic treatments against the virus.
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spelling pubmed-75139322020-09-25 Estimative of real number of infections by COVID-19 in Brazil and possible scenarios Cintra, H.P.C. Fontinele, F.N. Infect Dis Model Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu This paper attempts to provide methods to estimate the real scenario of the novel coronavirus pandemic in Brazil, specifically in the states of Sao Paulo, Pernambuco, Espirito Santo, Amazonas and the Federal District. By the use of a SEIRD mathematical model with age division, we predict the infection and death curves, stating the peak date for Brazil and above states. We also carry out a prediction for the ICU demand in these states and for how severe possible collapse in the local health system would be. Finally, we establish some future scenarios including the relaxation on social isolation and the introduction of vaccines and other efficient therapeutic treatments against the virus. KeAi Publishing 2020-09-24 /pmc/articles/PMC7513932/ /pubmed/32995682 http://dx.doi.org/10.1016/j.idm.2020.09.004 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu
Cintra, H.P.C.
Fontinele, F.N.
Estimative of real number of infections by COVID-19 in Brazil and possible scenarios
title Estimative of real number of infections by COVID-19 in Brazil and possible scenarios
title_full Estimative of real number of infections by COVID-19 in Brazil and possible scenarios
title_fullStr Estimative of real number of infections by COVID-19 in Brazil and possible scenarios
title_full_unstemmed Estimative of real number of infections by COVID-19 in Brazil and possible scenarios
title_short Estimative of real number of infections by COVID-19 in Brazil and possible scenarios
title_sort estimative of real number of infections by covid-19 in brazil and possible scenarios
topic Special issue on Modelling and Forecasting the 2019 Novel Coronavirus (2019-nCoV) Transmission; Edited by Prof. Carlos Castillo-Chavez, Prof. Gerardo Chowell-Puente, Prof. Ping Yan, Prof. Jianhong Wu
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513932/
https://www.ncbi.nlm.nih.gov/pubmed/32995682
http://dx.doi.org/10.1016/j.idm.2020.09.004
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