Structural Identifiability of Compartmental Models: Recent Progress and Future Directions

TL;DR

This paper reviews recent advances in the theory and application of structural identifiability in compartmental models, emphasizing new methods for analysis, reparametrization, and structural prediction across various fields.

Contribution

It summarizes recent progress in theoretical and algorithmic approaches for analyzing and reparametrizing unidentifiable models, especially in linear compartmental systems.

Findings

Recent algorithms for reparametrization of unidentifiable models

Methods for predicting identifiability from model structure

Applications in epidemiology and oncology

Abstract

We summarize recent progress on the theory and applications of structural identifiability of compartmental models. On the applications side, we review identifiability analyses undertaken recently for models arising in epidemiology, oncology, and other areas; and we summarize common approaches for handling models that are unidentifiable. We also highlight recent theoretical and algorithmic results on how to reparametrize unidentifiable models and, in the context of linear compartmental models, how to predict identifiability properties directly from the model structure. Finally, we highlight future research directions.