Structural Identifiability of Compartmental Models: Recent Progress and Future Directions
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866912468631027712 |
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| author | Meshkat, Nicolette Shiu, Anne |
| author_facet | Meshkat, Nicolette Shiu, Anne |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04496 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Structural Identifiability of Compartmental Models: Recent Progress and Future Directions Meshkat, Nicolette Shiu, Anne Methodology Dynamical Systems Quantitative Methods 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. |
| title | Structural Identifiability of Compartmental Models: Recent Progress and Future Directions |
| topic | Methodology Dynamical Systems Quantitative Methods |
| url | https://arxiv.org/abs/2507.04496 |