A practical identifiability criterion leveraging weak-form parameter estimation

TL;DR

This paper introduces a practical identifiability criterion based on noise and estimation error, utilizing a weak-form method for efficient, noise-robust parameter estimation in systems with unobserved variables.

Contribution

It proposes a new (e, q)-based identifiability criterion combined with a weak-form estimation approach for faster, noise-resilient parameter assessment.

Findings

The criterion effectively captures changes in estimate quality due to noise.

The weak-form method is computationally efficient and robust to noise.

Validated on classical biological models.

Abstract

In this work, we define a practical identifiability criterion, (e, q)-identifiability, based on a parameter e, reflecting the noise in observed variables, and a parameter q, reflecting the mean-square error of the parameter estimator. This criterion is better able to encompass changes in the quality of the parameter estimate due to increased noise in the data (compared to existing criteria based solely on average relative errors). Furthermore, we leverage a weak-form equation error-based method of parameter estimation for systems with unobserved variables to assess practical identifiability far more quickly in comparison to output error-based parameter estimation. We do so by generating weak-form input-output equations using differential algebra techniques, as previously proposed by Boulier et al [1], and then applying Weak form Estimation of Nonlinear Dynamics (WENDy) to obtain parameter estimates. This method is computationally efficient and robust to noise, as demonstrated through two classical biological modelling examples.