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Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases()

The novel of COVID-19 disease started in late 2019 making the worldwide governments came across a high number of critical and death cases, beyond constant fear of the collapse in their health systems. Since the beginning of the pandemic, researchers and authorities are mainly concerned with carrying...

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Autores principales: de Oliveira, Anderson Castro Soares, Morita, Lia Hanna Martins, da Silva, Eveliny Barroso, Zardo, Luiz André Ribeiro, Fontes, Cor Jesus Fernandes, Granzotto, Daniele Cristina Tita
Formato: Online Artículo Texto
Lenguaje:English
Publicado: KeAi Publishing 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7513875/
https://www.ncbi.nlm.nih.gov/pubmed/32995681
http://dx.doi.org/10.1016/j.idm.2020.09.005
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author de Oliveira, Anderson Castro Soares
Morita, Lia Hanna Martins
da Silva, Eveliny Barroso
Zardo, Luiz André Ribeiro
Fontes, Cor Jesus Fernandes
Granzotto, Daniele Cristina Tita
author_facet de Oliveira, Anderson Castro Soares
Morita, Lia Hanna Martins
da Silva, Eveliny Barroso
Zardo, Luiz André Ribeiro
Fontes, Cor Jesus Fernandes
Granzotto, Daniele Cristina Tita
author_sort de Oliveira, Anderson Castro Soares
collection PubMed
description The novel of COVID-19 disease started in late 2019 making the worldwide governments came across a high number of critical and death cases, beyond constant fear of the collapse in their health systems. Since the beginning of the pandemic, researchers and authorities are mainly concerned with carrying out quantitative studies (modeling and predictions) overcoming the scarcity of tests that lead us to under-reporting cases. To address these issues, we introduce a Bayesian approach to the SIR model with correction for under-reporting in the analysis of COVID-19 cases in Brazil. The proposed model was enforced to obtain estimates of important quantities such as the reproductive rate and the average infection period, along with the more likely date when the pandemic peak may occur. Several under-reporting scenarios were considered in the simulation study, showing how impacting is the lack of information in the modeling.
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spelling pubmed-75138752020-09-25 Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases() de Oliveira, Anderson Castro Soares Morita, Lia Hanna Martins da Silva, Eveliny Barroso Zardo, Luiz André Ribeiro Fontes, Cor Jesus Fernandes Granzotto, Daniele Cristina Tita 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 The novel of COVID-19 disease started in late 2019 making the worldwide governments came across a high number of critical and death cases, beyond constant fear of the collapse in their health systems. Since the beginning of the pandemic, researchers and authorities are mainly concerned with carrying out quantitative studies (modeling and predictions) overcoming the scarcity of tests that lead us to under-reporting cases. To address these issues, we introduce a Bayesian approach to the SIR model with correction for under-reporting in the analysis of COVID-19 cases in Brazil. The proposed model was enforced to obtain estimates of important quantities such as the reproductive rate and the average infection period, along with the more likely date when the pandemic peak may occur. Several under-reporting scenarios were considered in the simulation study, showing how impacting is the lack of information in the modeling. KeAi Publishing 2020-09-24 /pmc/articles/PMC7513875/ /pubmed/32995681 http://dx.doi.org/10.1016/j.idm.2020.09.005 Text en © 2020 The Authors. Production and hosting by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/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
de Oliveira, Anderson Castro Soares
Morita, Lia Hanna Martins
da Silva, Eveliny Barroso
Zardo, Luiz André Ribeiro
Fontes, Cor Jesus Fernandes
Granzotto, Daniele Cristina Tita
Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases()
title Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases()
title_full Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases()
title_fullStr Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases()
title_full_unstemmed Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases()
title_short Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases()
title_sort bayesian modeling of covid-19 cases with a correction to account for under-reported cases()
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/PMC7513875/
https://www.ncbi.nlm.nih.gov/pubmed/32995681
http://dx.doi.org/10.1016/j.idm.2020.09.005
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