Work

Structural identifiability of compartmental models: Recent progress and future directions

Year: 2025

Type: review

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.

Source: Current Opinion in Systems Biology

DOI: 10.1016/j.coisb.2025.100559

Authors: Nicolette Meshkat, Anne Shiu

Institutions: Santa Clara University, Texas A&M University

Language: English

Topic: Control Systems and Identification

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,170

Funders: National Science Foundation, Directorate for Mathematical and Physical Sciences