ArticlePloS one2016
Analysis of Practical Identifiability of a Viral Infection Model.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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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.
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Who cites it
23 citing papers in PubMed.
- A Practical Identifiability Criterion Leveraging Weak-Form Parameter Estimation.Bulletin of mathematical biology · 2026Article
- Random time-shift approximation enables hierarchical Bayesian inference of mechanistic within-host viral dynamics models on large datasets.PLoS computational biology · 2025Article
- Structural and practical identifiability of within-host models of virus dynamics - a review.Current opinion in systems biology · 2025Article
- CrossLabFit: A novel framework for integrating qualitative and quantitative data across multiple labs for model calibration.PLoS computational biology · 2025Article
- Article
- Analyzing Differences in Viral Dynamics Between Vaccinated and Unvaccinated RSV Patients.Epidemiologia (Basel, Switzerland) · 2025Article
- Modeling the CD8＋ T cell immune response to influenza infection in adult and aged mice.Journal of theoretical biology · 2024Article
- How robust are estimates of key parameters in standard viral dynamic models?PLoS computational biology · 2024Article
- Sloppiness: Fundamental study, new formalism and its application in model assessment.PloS one · 2023Article
- Modelling of spatial infection spread through heterogeneous population: from lattice to partial differential equation models.Royal Society open science · 2022Article
- Model-informed drug repurposing: Viral kinetic modelling to prioritize rational drug combinations for COVID-19.British journal of clinical pharmacology · 2021Article
- Leveraging Structured Biological Knowledge for Counterfactual Inference: A Case Study of Viral Pathogenesis.IEEE transactions on big data · 2021Article
- Topological data analysis to model the shape of immune responses during co-infections.Communications in nonlinear science & numerical simulation · 2020Article
- A theoretical framework for transitioning from patient-level to population-scale epidemiological dynamics: influenza A as a case study.Journal of the Royal Society, Interface · 2020Article
- In-host Mathematical Modelling of COVID-19 in Humans.Annual reviews in control · 2020Article
- Modeling Quorum Sensing Dynamics and Interference onFrontiers in microbiology · 2019Article
- Mathematical modeling of within-host Zika virus dynamics.Immunological reviews · 2018Review
- Host-pathogen kinetics during influenza infection and coinfection: insights from predictive modeling.Immunological reviews · 2018Review
- High-resolution epidemic simulation using within-host infection and contact data.BMC public health · 2018Article
- MicroRNA-381 protects myocardial cell function in children and mice with viral myocarditis via targeting cyclooxygenase-2 expression.Experimental and therapeutic medicine · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mathematical modelling approaches have granted a significant contribution to life sciences and beyond to understand experimental results. However, incomplete and inadequate assessments in parameter estimation practices hamper the parameter reliability, and consequently the insights that ultimately could arise from a mathematical model. To keep the diligent works in modelling biological systems from being mistrusted, potential sources of error must be acknowledged. Employing a popular mathematical model in viral infection research, existing means and practices in parameter estimation are exemplified. Numerical results show that poor experimental data is a main source that can lead to erroneous parameter estimates despite the use of innovative parameter estimation algorithms. Arbitrary choices of initial conditions as well as data asynchrony distort the parameter estimates but are often overlooked in modelling studies. This work stresses the existence of several sources of error buried in reports of modelling biological systems, voicing the need for assessing the sources of error, consolidating efforts in solving the immediate difficulties, and possibly reconsidering the use of mathematical modelling to quantify experimental data.
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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.