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A Tutorial on Structural Identifiability of Epidemic Models Using StructuralIdentifiability.j1.

Yuganthi R Liyanage1, Omar Saucedo2, Necibe Tuncer1

  • 1Department of Mathematics and Statistics, Florida Atlantic University, , Boca Raton, 33431, Florida, USA.

Arxiv
|April 27, 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 framework and visual tools to integrate this analysis into workflows, improving parameter estimation and interdisciplinary communication.

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.
  • Lack of standardized methods hinders reliable calibration and inference.

Purpose of the Study:

  • To provide a user-oriented tutorial on systematically integrating global structural identifiability analysis into epidemic modeling.
  • To present a reproducible framework using the Julia package StructuralIdentifiability.jl for ordinary differential equation models.
  • To enhance interpretation and communication of identifiability results through visual strategies.

Main Methods:

  • Demonstration of a workflow for global structural identifiability analysis.
  • Application of the framework to various epidemic models (SEIR variants, vector-borne, hospitalization, mortality).
  • Development of a visual communication strategy embedding results into compartmental diagrams.

Main Results:

  • Identifiability is highly dependent on model structure, observed variables, and initial conditions.
  • Identifiable parameter combinations can exist even when individual parameters are not globally identifiable.
  • The framework ensures transparency, reproducibility, and facilitates comparative insights across different model classes.

Conclusions:

  • This work offers practical guidance and a teaching resource for incorporating structural identifiability analysis into epidemic model development.
  • The provided framework and visual tools aim to increase the consistent and effective use of identifiability analysis.
  • Publicly available code and diagrams promote reproducibility and reuse in infectious disease modeling research.