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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.
Article in English | 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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Full text: Available Collection: International databases Database: MEDLINE Main subject: Communicable Diseases / Epidemics Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: J Math Biol Year: 2022 Document Type: Article Affiliation country: S00285-022-01804-5

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Communicable Diseases / Epidemics Type of study: Observational study / Prognostic study Limits: Humans Language: English Journal: J Math Biol Year: 2022 Document Type: Article Affiliation country: S00285-022-01804-5