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<p>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 relevance in model calibration and inference, structural identifiability analysis remains underused and inconsistently applied in the infectious disease modeling literature. This paper presents a user-oriented methodological tutorial that demonstrates how global structural identifiability analysis can be systematically integrated into epidemic modeling workflows. We provide a reproducible workflow for conducting structural identifiability analysis of ordinary differential equation models using the Julia package <i>StructuralIdentifiability.jl</i>. We illustrate this workflow across a range of commonly used epidemic models, including SEIR variants with asymptomatic and presymptomatic transmission, vector-borne disease models, and systems incorporating hospitalization and disease-induced mortality. In addition to hands-on methodological guidance, we introduce a novel visual communication strategy that embeds identifiability results directly into compartmental diagrams, facilitating interpretation and interdisciplinary communication. Our results illustrate, through a unified and reproducible workflow, how identifiability depends critically on model structure, the choice of observed variables, and assumptions about initial conditions, and how identifiable parameter combinations can be recovered even when individual parameters are not globally identifiable. Rather than introducing new identifiability theory, this work focuses on the practical implementation, interpretation, and communication of existing methods in applied epidemic modeling contexts. By combining practical instruction, comparative insights across model classes, and enhanced visualization tools, this work serves as both a reference and a teaching resource for researchers and educators seeking to incorporate structural identifiability analysis into epidemic model design and interpretation. All code and annotated diagrams are publicly available to support reproducibility and reuse.</p>

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

  • 期刊论文

  • 发表日期：

    2026年6月3日

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  • Yuganthi R. Liyanage,
  • Omar Saucedo,
  • Necibe Tuncer,
  • Gerardo Chowell

摘要

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 relevance in model calibration and inference, structural identifiability analysis remains underused and inconsistently applied in the infectious disease modeling literature. This paper presents a user-oriented methodological tutorial that demonstrates how global structural identifiability analysis can be systematically integrated into epidemic modeling workflows. We provide a reproducible workflow for conducting structural identifiability analysis of ordinary differential equation models using the Julia package StructuralIdentifiability.jl. We illustrate this workflow across a range of commonly used epidemic models, including SEIR variants with asymptomatic and presymptomatic transmission, vector-borne disease models, and systems incorporating hospitalization and disease-induced mortality. In addition to hands-on methodological guidance, we introduce a novel visual communication strategy that embeds identifiability results directly into compartmental diagrams, facilitating interpretation and interdisciplinary communication. Our results illustrate, through a unified and reproducible workflow, how identifiability depends critically on model structure, the choice of observed variables, and assumptions about initial conditions, and how identifiable parameter combinations can be recovered even when individual parameters are not globally identifiable. Rather than introducing new identifiability theory, this work focuses on the practical implementation, interpretation, and communication of existing methods in applied epidemic modeling contexts. By combining practical instruction, comparative insights across model classes, and enhanced visualization tools, this work serves as both a reference and a teaching resource for researchers and educators seeking to incorporate structural identifiability analysis into epidemic model design and interpretation. All code and annotated diagrams are publicly available to support reproducibility and reuse.