Limits of epidemic prediction using SIR models.

Journal Article (Journal Article)

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.

Full Text

Duke Authors

Cited Authors

  • Melikechi, O; Young, AL; Tang, T; Bowman, T; Dunson, D; Johndrow, J

Published Date

  • September 2022

Published In

Volume / Issue

  • 85 / 4

Start / End Page

  • 36 -

PubMed ID

  • 36125562

Pubmed Central ID

  • PMC9487859

Electronic International Standard Serial Number (EISSN)

  • 1432-1416

International Standard Serial Number (ISSN)

  • 0303-6812

Digital Object Identifier (DOI)

  • 10.1007/s00285-022-01804-5


  • eng