A model to predict SARS-CoV-2 infection based on the first three-month surveillance data in Brazil (2020)
- Authors:
- USP affiliated authors: QUIJANO, FREDI ALEXANDER DIAZ - FSP ; VARELA, ANDREA LILIANA VESGA - FSP ; SILVA, JOSÉ MÁRIO NUNES DA - FSP
- Unidade: FSP
- DOI: 10.1101/2020.04.05.20047944
- Subjects: CORONAVIRUS; VIGILÂNCIA EPIDEMIOLÓGICA; REGRESSÃO LINEAR; DIAGNÓSTICO CLÍNICO; PRECISÃO DO TESTE
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Source:
- Título: medRxiv (The Preprint Server For Health Sciences)
- Volume/Número/Paginação/Ano: Eprint, [13p.], 2020
- Este periódico é de assinatura
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: green
- Licença: other-oa
-
ABNT
DIAZ-QUIJANO, Fredi Alexander et al. A model to predict SARS-CoV-2 infection based on the first three-month surveillance data in Brazil. medRxiv (The Preprint Server For Health Sciences), p. [13], 2020Tradução . . Disponível em: https://doi.org/10.1101/2020.04.05.20047944. Acesso em: 10 out. 2024. -
APA
Diaz-Quijano, F. A., Silva, J. M. N. da, Ganem, F., Oliveira, S., Vesga-Varela, A. L., & Croda, J. (2020). A model to predict SARS-CoV-2 infection based on the first three-month surveillance data in Brazil. medRxiv (The Preprint Server For Health Sciences), [13]. doi:10.1101/2020.04.05.20047944 -
NLM
Diaz-Quijano FA, Silva JMN da, Ganem F, Oliveira S, Vesga-Varela AL, Croda J. A model to predict SARS-CoV-2 infection based on the first three-month surveillance data in Brazil [Internet]. medRxiv (The Preprint Server For Health Sciences). 2020 ; [13].[citado 2024 out. 10 ] Available from: https://doi.org/10.1101/2020.04.05.20047944 -
Vancouver
Diaz-Quijano FA, Silva JMN da, Ganem F, Oliveira S, Vesga-Varela AL, Croda J. A model to predict SARS-CoV-2 infection based on the first three-month surveillance data in Brazil [Internet]. medRxiv (The Preprint Server For Health Sciences). 2020 ; [13].[citado 2024 out. 10 ] Available from: https://doi.org/10.1101/2020.04.05.20047944 - How feasible is it to abandon statistical significance? A reflection based on a short survey
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Informações sobre o DOI: 10.1101/2020.04.05.20047944 (Fonte: oaDOI API)
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