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Practical Identifiability and Parameter Estimation of Compartmental Epidemiological Models by Chen, Q. Y.; Rapti, Z.; Drossinos, Y.; Cuevas-Maraver, J.; Kevrekidis, G. A.; Kevrekidis, P. G. is a scholarly article available to read on EtoBox.

What is Practical Identifiability and Parameter Estimation of Compartmental Epidemiological Models about?

Practical parameter identifiability in ODE-based epidemiological models is a known issue, yet one that merits further study. It is essentially ubiquitous due to noise and errors in real data. In this study, to avoid uncertainty stemming from data of unknown quality, simulated data with added noise are used to investigate practical identifiability in two distinct epidemiological models. Particular emphasis is placed on the role of initial conditions, which are assumed unknown, except those that are directly measured. Instead of just focusing on one method of estimation, we use and compare results from various broadly used methods, including maximum likelihood and Markov Chain Monte Carlo (MCMC) estimation. Among other findings, our analysis revealed that the MCMC estimator is overall more robust than the point estimators considered. Its estimates and predictions are improved when the initial conditions of certain compartments are fixed so that the model becomes globally identifiable. For the point estimators, whether fixing or fitting the that are not directly measured improves parameter estimates is model-dependent. Specifically, in the standard SEIR model, fixing the initial condi

Author

Chen, Q. Y.; Rapti, Z.; Drossinos, Y.; Cuevas-Maraver, J.; Kevrekidis, G. A.; Kevrekidis, P. G.

Published

2024

Language

EN