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Limits of epidemic prediction using SIR models.
Melikechi, Omar; Young, Alexander L; Tang, Tao; Bowman, Trevor; Dunson, David; Johndrow, James.
  • Melikechi O; Department of Mathematics, Duke University, Durham, NC, USA. omar.melikechi@duke.edu.
  • Young AL; Department of Statistics, Harvard University, Cambridge, MA, USA.
  • Tang T; Department of Mathematics, Duke University, Durham, NC, USA.
  • Bowman T; Department of Mathematics, Duke University, Durham, NC, USA.
  • Dunson D; Department of Mathematics, Duke University, Durham, NC, USA.
  • Johndrow J; Department of Statistics, Duke University, Durham, NC, USA.
J Math Biol ; 85(4): 36, 2022 09 20.
Artículo en Inglés | MEDLINE | ID: covidwho-2048225
ABSTRACT
The Susceptible-Infectious-Recovered (SIR) equations and their extensions comprise a commonly utilized set of models for understanding and predicting the course of an epidemic. In practice, it is of substantial interest to estimate the model parameters based on noisy observations early in the outbreak, well before the epidemic reaches its peak. This allows prediction of the subsequent course of the epidemic and design of appropriate interventions. However, accurately inferring SIR model parameters in such scenarios is problematic. This article provides novel, theoretical insight on this issue of practical identifiability of the SIR model. Our theory provides new understanding of the inferential limits of routinely used epidemic models and provides a valuable addition to current simulate-and-check methods. We illustrate some practical implications through application to a real-world epidemic data set.
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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: Enfermedades Transmisibles / Epidemias Tipo de estudio: Estudio observacional / Estudio pronóstico Límite: Humanos Idioma: Inglés Revista: J Math Biol Año: 2022 Tipo del documento: Artículo País de afiliación: S00285-022-01804-5

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Texto completo: Disponible Colección: Bases de datos internacionales Base de datos: MEDLINE Asunto principal: Enfermedades Transmisibles / Epidemias Tipo de estudio: Estudio observacional / Estudio pronóstico Límite: Humanos Idioma: Inglés Revista: J Math Biol Año: 2022 Tipo del documento: Artículo País de afiliación: S00285-022-01804-5