A Tutorial on Structural Identifiability of Epidemic Models Using StructuralIdentifiability.jl

[2505.10517] A Tutorial on Structural Identifiability of Epidemic Models Using StructuralIdentifiability.jl Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantitative Biology > Quantitative Methods arXiv:2505.10517 (q-bio) [Submitted on 15 May 2025 ( v1 ), last revised 13 Apr 2026 (this version, v5)] Title: A Tutorial on Structural Identifiability of Epidemic Models Using StructuralIdentifiability.jl Authors: Yuganthi R. Liyanage , Omar Saucedo , Necibe Tuncer , Gerardo Chowell View a PDF of the paper titled A Tutorial on Structural Identifiability of Epidemic Models Using StructuralIdentifiability.jl, by Yuganthi R. Liyanage and 3 other authors View PDF HTML (experimental) Abstract: Structural identifiability is the theoretical ability to uniquely recover model parameters from ideal, noise-free data and is a prerequisite for reliable parameter estimation in epidemic modeling. Despite its importance for calibration and inference, structural identifiability analysis remains underused and inconsistently applied in infectious disease modeling. This paper presents a user-oriented methodological tutorial demonstrating how global structural identifiability analysis can be systematically integrated into epidemic modeling workflows. We provide a reproducible framework for conducting structural identifiability analysis of ordinary differential equation models using the Julia package this http URL . The workflow is illustrated across commonly used epidemic models, including SEIR variants with asymptomatic and presymptomatic transmission, vector-borne disease models, and systems incorporating hospitalization and disease-induced mortality. We also introduce a visual communication strategy that embeds identifiability results directly into compartmental diagrams, facilitating interpretation and interdisciplinary communication. Our results show that identifiability depends critically on model structure, the choice of observed variables, and assumptions about initial conditions, and that identifiable parameter combinations may exist even when individual parameters are not globally identifiable. Emphasizing transparent implementation, interpretation, and communication, this work provides practical guidance and comparative insights across model classes. The tutorial is designed as both a reference and a teaching resource for researchers and educators seeking to incorporate structural identifiability analysis into epidemic model development. All code and annotated diagrams are publicly available to ensure reproducibility and reuse. Subjects: Quantitative Methods (q-bio.QM) Cite as: arXiv:2505.10517 [q-bio.QM] (or arXiv:2505.10517v5 [q-bio.QM] for this version) https://doi.org/10.48550/arXiv.2505.10517 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Gerardo Chowell [ view email ] [v1] Thu, 15 May 2025 17:22:47 UTC (2,820 KB) [v2] Tue, 20 May 2025 18:41:29 UTC (841 KB) [v3] Sat, 11 Oct 2025 21:18:48 UTC (636 KB) [v4] Sat, 14 Feb 2026 13:14:54 UTC (673 KB) [v5] Mon, 13 Apr 2026 20:17:11 UTC (676 KB) Full-text links: Access Paper: View a PDF of the paper titled A Tutorial on Structural Identifiability of Epidemic Models Using StructuralIdentifiability.jl, by Yuganthi R. Liyanage and 3 other authors View PDF HTML (experimental) TeX Source view license Current browse context: q-bio.QM < prev | next > new | recent | 2025-05 Change to browse by: q-bio References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... 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