Identifiability investigation of within-host models of acute virus infection

Yuganthi R. Liyanage; Nora Heitzman-Breen; Necibe Tuncer; Stanca M. Ciupe; Yuganthi R. Liyanage; Nora Heitzman-Breen; Necibe Tuncer; Stanca M. Ciupe

doi:10.3934/mbe.2024325

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 10: 7394-7420. doi: 10.3934/mbe.2024325

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Identifiability investigation of within-host models of acute virus infection

1.
Department of Mathematics and Statistics, Florida Atlantic University, Boca Raton, FL, USA
2.
Department of Mathematics, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
3.
Virginia Tech Center for the Mathematics of Biosystems, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA

† These authors contributed equally to this work.
Academic Editor: Shigui Ruan

Received: 29 July 2024 Revised: 24 September 2024 Accepted: 09 October 2024 Published: 28 October 2024

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.
- influenza virus,
- mathematical modeling,
- structural identifiability,
- profile likelihood,
- optimal experimental design
Citation: Yuganthi R. Liyanage, Nora Heitzman-Breen, Necibe Tuncer, Stanca M. Ciupe. Identifiability investigation of within-host models of acute virus infection[J]. Mathematical Biosciences and Engineering, 2024, 21(10): 7394-7420. doi: 10.3934/mbe.2024325

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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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