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

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 StructuralIdentifiability.jl. 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.