Your browser doesn't support javascript.
loading
Time-dependent vaccine efficacy estimation quantified by a mathematical model.
Loria, Jennifer; Albani, Vinicius V L; Coutinho, Francisco A B; Covas, Dimas T; Struchiner, Claudio J; Zubelli, Jorge P; Massad, Eduardo.
Afiliación
  • Loria J; Instituto de Matemática Pura e Aplicada, Rio de Janeiro, Brazil.
  • Albani VVL; School of Mathematics, Universidad de Costa Rica, San José, Costa Rica.
  • Coutinho FAB; LAMMCA, Department of Mathematics, Federal University of Santa Catarina, Florianopolis, Brazil.
  • Covas DT; Department of Pathology, University of São Paulo, São Paulo, Brazil.
  • Struchiner CJ; Instituto Butantan, São Paulo, Brazil.
  • Zubelli JP; School of Applied Mathematics, Fundação Getúlio Vargas, Rio de Janeiro, Brazil.
  • Massad E; Mathematics Department, Khalifa University, Abu Dhabi, UAE.
PLoS One ; 18(5): e0285466, 2023.
Article en En | MEDLINE | ID: mdl-37167285
In this paper we calculate the variation of the estimated vaccine efficacy (VE) due to the time-dependent force of infection resulting from the difference between the moment the Clinical Trial (CT) begins and the peak in the outbreak intensity. Using a simple mathematical model we tested the hypothesis that the time difference between the moment the CT begins and the peak in the outbreak intensity determines substantially different values for VE. We exemplify the method with the case of the VE efficacy estimation for one of the vaccines against the new coronavirus SARS-CoV-2.
Asunto(s)
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Clinical_trials Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2023 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: COVID-19 Tipo de estudio: Clinical_trials Límite: Humans Idioma: En Revista: PLoS One Asunto de la revista: CIENCIA / MEDICINA Año: 2023 Tipo del documento: Article País de afiliación: Brasil Pais de publicación: Estados Unidos