http://repositorio.unb.br/handle/10482/39846
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ARTIGO_TenEpidemiologicalParameters.pdf | 693,37 kB | Adobe PDF | View/Open |
Title: | Ten epidemiological parameters of COVID-19 : use of rapid literature review to inform predictive models during the pandemic |
Authors: | Gallo, Luciana Guerra Oliveira, Ana Flávia de Morais Abrahão, Amanda Amaral Sandoval, Leticia Assad Maia Martins, Yure Rodrigues Araújo Almirón, Maria Santos, Fabiana Sherine Ganem dos Araújo, Wildo Navegantes Oliveira, Maria Regina Fernandes de Peixoto, Henry Maia |
Assunto:: | Infecções por coronavírus Covid-19 Estatística |
Issue Date: | 1-Dec-2020 |
Publisher: | Frontiers |
Citation: | GALLO, Luciana Guerra et al. Ten epidemiological parameters of COVID-19: use of rapid literature review to inform predictive models during the pandemic. Frontiers in Public Health, v. 8, art. 598547, dez. 2020. DOI: https://doi.org/10.3389/fpubh.2020.598547. Disponível em: https://www.frontiersin.org/articles/10.3389/fpubh.2020.598547/full. Acesso em: 31 dez. 2020. |
Abstract: | Objective: To describe the methods used in a rapid review of the literature and to present the main epidemiological parameters that describe the transmission of SARS-Cov-2 and the illness caused by this virus, coronavirus disease 2019 (COVID-19). Methods: This is a methodological protocol that enabled a rapid review of COVID-19 epidemiological parameters. Findings: The protocol consisted of the following steps: definition of scope; eligibility criteria; information sources; search strategies; selection of studies; and data extraction. Four reviewers and three supervisors conducted this review in 40 days. Of the 1,266 studies found, 65 were included, mostly observational and descriptive in content, indicating relative homogeneity as to the quality of the evidence. The variation in the basic reproduction number, between 0.48 and 14.8; and the median of the hospitalization period, between 7.5 and 20.5 days stand out as key findings. Conclusion: We identified and synthesized 10 epidemiological parameters that may support predictive models and other rapid reviews to inform modeling of this and other future public health emergencies. |
metadata.dc.description.unidade: | Faculdade de Medicina (FMD) |
Licença:: | Copyright © 2020 Gallo, Oliveira, Abrahão, Sandoval, Martins, Almirón, dos Santos, Araújo, de Oliveira and Peixoto. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
DOI: | https://doi.org/10.3389/fpubh.2020.598547 |
Appears in Collections: | Artigos publicados em periódicos e afins UnB - Covid-19 |
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