ArticleJournal of mathematical biology2022
Structural identifiability analysis of age-structured PDE epidemic models.
Article in Journal of mathematical biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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.
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.
Who cites it
17 citing papers in PubMed.
- The eight pillars of within-host tuberculosis modelling.Frontiers in immunology · 2026Review
- Structural and practical identifiability of within-host models of virus dynamics - a review.Current opinion in systems biology · 2025Article
- Assessing parameter identifiability of a hemodynamics PDE model using spectral surrogates and dimension reduction.PLoS computational biology · 2025Article
- Characterising the Behaviour of a Structured PDE Model of the Cell Cycle in Contrast to a Corresponding ODE System.Bulletin of mathematical biology · 2025Article
- Structural identifiability of linear-in-parameter parabolic PDEs through auxiliary elliptic operators.Journal of mathematical biology · 2025Article
- Structural and Practical Identifiability of Phenomenological Growth Models for Epidemic Forecasting.Viruses · 2025Article
- Quantifying cell cycle regulation by tissue crowding.Biophysical journal · 2025Article
- Optimal control of combination immunotherapy for a virtual murine cohort in a glioblastoma-immune dynamics model.Journal of theoretical biology · 2024Article
- Optimal control of combination immunotherapy for a virtual murine cohort in a glioblastoma-immune dynamics model.bioRxiv : the preprint server for biology · 2024Article
- Parameter identifiability and model selection for partial differential equation models of cell invasion.Journal of the Royal Society, Interface · 2024Article
- Implementing measurement error models with mechanistic mathematical models in a likelihood-based framework for estimation, identifiability analysis and prediction in the life sciences.Journal of the Royal Society, Interface · 2024Review
- A computational approach to identifiability analysis for a model of the propagation and control of COVID-19 in Chile.Journal of biological dynamics · 2023Article
- Quantifying tissue growth, shape and collision via continuum models and Bayesian inference.Journal of the Royal Society, Interface · 2023Article
- Practical Understanding of Cancer Model Identifiability in Clinical Applications.Life (Basel, Switzerland) · 2023Article
- Computationally efficient framework for diagnosing, understanding and predicting biphasic population growth.Journal of the Royal Society, Interface · 2022Article
- Efficient inference and identifiability analysis for differential equation models with random parameters.PLoS computational biology · 2022Article
- A protocol for dynamic model calibration.Briefings in bioinformatics · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Computational and mathematical models rely heavily on estimated parameter values for model development. Identifiability analysis determines how well the parameters of a model can be estimated from experimental data. Identifiability analysis is crucial for interpreting and determining confidence in model parameter values and to provide biologically relevant predictions. Structural identifiability analysis, in which one assumes data to be noiseless and arbitrarily fine-grained, has been extensively studied in the context of ordinary differential equation (ODE) models, but has not yet been widely explored for age-structured partial differential equation (PDE) models. These models present additional difficulties due to increased number of variables and partial derivatives as well as the presence of boundary conditions. In this work, we establish a pipeline for structural identifiability analysis of age-structured PDE models using a differential algebra framework and derive identifiability results for specific age-structured models. We use epidemic models to demonstrate this framework because of their wide-spread use in many different diseases and for the corresponding parallel work previously done for ODEs. In our application of the identifiability analysis pipeline, we focus on a Susceptible-Exposed-Infected model for which we compare identifiability results for a PDE and corresponding ODE system and explore effects of age-dependent parameters on identifiability. We also show how practical identifiability analysis can be applied in this example.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.