ArticleMathematical biosciences and engineering : MBE2024
Identifiability investigation of within-host models of acute virus infection.
Article in Mathematical biosciences and engineering : MBE, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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.
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Who cites it
8 citing papers in PubMed.
- In Vitro Analysis and Dynamic Modeling of SARS-CoV-2 Infection Inhibition by Sigma-1 Receptor Antagonist PB28.Bulletin of mathematical biology · 2026Article
- A Practical Identifiability Criterion Leveraging Weak-Form Parameter Estimation.Bulletin of mathematical biology · 2026Article
- Mathematical Modeling of the Role of Cytokines in Sindbis Virus Treatment of Glioblastoma.CPT: pharmacometrics & systems pharmacology · 2026Article
- Mathematical Modeling of Cell-Cell Fusion Assays to Characterize Virus-Mediated Cell Fusion.Advances in experimental medicine and biology · 2026Review
- Structural and practical identifiability of within-host models of virus dynamics - a review.Current opinion in systems biology · 2025Article
- From Bird Viremia to Bird Surveillance: Identifiability in a Multiscale Vector-Borne Model of Usutu Virus Infection.bioRxiv : the preprint server for biology · 2025Article
- Article
- Analyzing Differences in Viral Dynamics Between Vaccinated and Unvaccinated RSV Patients.Epidemiologia (Basel, Switzerland) · 2025Article
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Authors and funding
4 authors.
Funding
Abstract
Uncertainty in parameter estimates from fitting within-host models to empirical data limits the model's ability to uncover mechanisms of infection, disease progression, and to guide pharmaceutical interventions. Understanding the effect of model structure and data availability on model predictions is important for informing model development and experimental design. To address sources of uncertainty in parameter estimation, we used four mathematical models of influenza A infection with increased degrees of biological realism. We tested the ability of each model to reveal its parameters in the presence of unlimited data by performing structural identifiability analyses. We then refined the results by predicting practical identifiability of parameters under daily influenza A virus titers alone or together with daily adaptive immune cell data. Using these approaches, we presented insight into the sources of uncertainty in parameter estimation and provided guidelines for the types of model assumptions, optimal experimental design, and biological information needed for improved predictions.
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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.