@article{Saucedo2024, 
author = {Omar Saucedo and Amanda Laubmeier and Tingting Tang and Benjamin Levy and Lale Asik and Tim Pollington and Olivia Prosper Feldman},
title = {Comparative analysis of practical identifiability methods for an SEIR model},
year = {2024},
journal = {AIMS Mathematics},
volume = {9},
number = {9},
pages = {24722-24761},
keywords = {identifiability, parameter estimation, Monte Carlo, correlation matrix, epidemiological data, mathematical modeling},
url = {https://www.sciopen.com/article/10.3934/math.20241204},
doi = {10.3934/math.20241204},
abstract = {Identifiability of a mathematical model plays a crucial role in the parameterization of the model. In this study, we established the structural identifiability of a susceptible-exposed-infected-recovered (SEIR) model given different combinations of input data and investigated practical identifiability with respect to different observable data, data frequency, and noise distributions. The practical identifiability was explored by both Monte Carlo simulations and a correlation matrix approach. Our results showed that practical identifiability benefits from higher data frequency and data from the peak of an outbreak. The incidence data gave the best practical identifiability results compared to prevalence and cumulative data. In addition, we compared and distinguished the practical identifiability by Monte Carlo simulations and a correlation matrix approach, providing insights into when to use which method for other applications.}
}