Evidence map›Paper›PMID 42038415›Full record

ArticleArXiv2026

A Tutorial on Structural Identifiability of Epidemic Models Using

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

Abstract readPreprint
PubMed Full text at PMC
In one paragraph

Article in ArXiv, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Yuganthi R LiyanageDepartment of Mathematics and Statistics, Florida Atlantic University, , Boca Raton, 33431, Florida, USA.
Omar SaucedoDepartment of Mathematics, Virginia Tech, , Blacksburg, 24060, Virginia, USA.
Necibe TuncerDepartment of Mathematics and Statistics, Florida Atlantic University, , Boca Raton, 33431, Florida, USA.
Gerardo ChowellSchool of Public Health, Georgia State University, , Atlanta, 30303, Georgia, USA.

Funding

Collaborative Research: DMS/NIGMS 1: Identifiability investigation of Multi-scale Models of Infectious DiseasesR01GM152743 · NIGMS · VIRGINIA POLYTECHNIC INST AND ST UNIV · PI CIUPE, STANCA M. · 2023 to 2025
$600k
NIGMS NIH HHS R01 GM152743
6 · The paper itself

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 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

Indexed as

DAISYEpidemic modelingParameter estimationPractical identifiabilityStructural identifiabilityStructuralIdentifiability.jl

Identifiers

PMID42038415
PMCPMC13105210

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.