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A Tutorial on Structural Identifiability of Epidemic Models Using StructuralIdentifiability.jl
Yuganthi R Liyanage1,2, Omar Saucedo3, Necibe Tuncer4
1Department of Mathematics and Statistics, Florida Atlantic University, Boca Raton, 33431, Florida, USA. aliyana2@asu.edu.
Bulletin of Mathematical Biology
|June 3, 2026
Summary
Structural identifiability analysis is crucial for reliable epidemic modeling but is underused. This tutorial provides a reproducible workflow using Julia for systematic integration and interpretation of identifiability in infectious disease models.
Area of Science:
- Epidemiology
- Mathematical Biology
- Computational Science
Background:
- Structural identifiability is essential for accurate parameter estimation in epidemic models.
- Current application of structural identifiability analysis in infectious disease modeling is inconsistent and underutilized.
- A systematic approach is needed to integrate identifiability analysis into epidemic modeling workflows.
Purpose of the Study:
- To provide a user-oriented tutorial on systematically integrating global structural identifiability analysis into epidemic modeling.
- To demonstrate a reproducible workflow for conducting this analysis using the Julia package StructuralIdentifiability.jl.
- To enhance the interpretation and communication of identifiability results through novel visualization strategies.
Main Methods:
- Development of a reproducible workflow for structural identifiability analysis of ordinary differential equation models.
- Application of the workflow to diverse epidemic models (SEIR variants, vector-borne, hospitalization, mortality).
- Introduction of a visual communication strategy embedding identifiability results into compartmental diagrams.
Main Results:
- Identifiability is critically dependent on model structure, observed variables, and initial conditions.
- The workflow successfully illustrates how identifiable parameter combinations can be recovered even when individual parameters are not globally identifiable.
- The study provides practical insights across various epidemic model classes.
Conclusions:
- This work offers a practical, reproducible method for structural identifiability analysis in epidemic modeling.
- The tutorial and visualization tools serve as a reference for researchers and educators.
- Enhanced integration of identifiability analysis improves epidemic model design, interpretation, and interdisciplinary communication.
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