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  1. Article ; Online: 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

    Infectious Disease Modelling

    2020  Volume 5, Page(s) 699–713

    Abstract: ... reporting in the analysis of COVID-19 cases in Brazil. The proposed model was enforced to obtain estimates ... 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 ...

    Abstract 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.
    Keywords covid19
    Language English
    Publishing date 2020-09-24
    Publishing country China
    Document type Journal Article
    ZDB-ID 3015225-2
    ISSN 2468-0427 ; 2468-2152
    ISSN (online) 2468-0427
    ISSN 2468-2152
    DOI 10.1016/j.idm.2020.09.005
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: 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

    Infectious Disease Modelling

    2020  Volume 5, Page(s) 699–713

    Keywords covid19
    Language English
    Publisher Elsevier BV
    Publishing country us
    Document type Article ; Online
    ZDB-ID 3015225-2
    ISSN 2468-0427 ; 2468-2152
    ISSN (online) 2468-0427
    ISSN 2468-2152
    DOI 10.1016/j.idm.2020.09.005
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases

    Anderson Castro Soares de Oliveira / Lia Hanna Martins Morita / Eveliny Barroso da Silva / Luiz André Ribeiro Zardo / Cor Jesus Fernandes Fontes / Daniele Cristina Tita Granzotto

    Infectious Disease Modelling, Vol 5, Iss , Pp 699-

    2020  Volume 713

    Abstract: ... reporting in the analysis of COVID-19 cases in Brazil. The proposed model was enforced to obtain estimates ... 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 ...

    Abstract 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.
    Keywords COVID-19 ; Under-reporting ; SIR model ; Bayesian aproach ; Infectious and parasitic diseases ; RC109-216 ; covid19
    Language English
    Publishing date 2020-01-01T00:00:00Z
    Publisher KeAi
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: Bayesian modeling of COVID-19 cases with a correction to account for under-reported cases

    Oliveira, A. C. S. d. / Morita, L. H. M. / da Silva, E. B. / Granzotto, D. C. T. / Zardo, L. A. R. / Fontes, C. J. F.

    Abstract: ... reporting in the analysis of COVID-19 cases in Brazil. The proposed model was enforced to obtain estimates ... 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 ...

    Abstract 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.
    Keywords covid19
    Publisher MedRxiv; WHO
    Document type Article ; Online
    DOI 10.1101/2020.05.24.20112029
    Database COVID19

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