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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
PubMed
Summary
This summary is machine-generated.

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.

Keywords:
DAISYEpidemic modelingParameter estimationPractical identifiabilityStructural identifiabilityStructuralIdentifiability.jl

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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.