• DOI:10.1007/s00285-022-01804-5
• Corpus ID: 245131172

Limits of epidemic prediction using SIR models

@article{Melikechi2021LimitsOE,
  title={Limits of epidemic prediction using SIR models},
  author={Omar Melikechi and Alexander L. Young and Tao-Qian Tang and Trevor Bowman and David B. Dunson and James E. Johndrow},
  journal={Journal of Mathematical Biology},
  year={2021},
  volume={85}
}

• Omar Melikechi, Alexander L. Young, +3 authors J. Johndrow
• Published 13 December 2021
• Mathematics
• Journal of Mathematical Biology

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.