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• W2113105229 abstract "Ordinary differential equations (ODE) are a powerful tool for modeling dynamic processes with wide applications in a variety of scientific fields. Over the last 2 decades, ODEs have also emerged as a prevailing tool in various biomedical research fields, especially in infectious disease modeling. In practice, it is important and necessary to determine unknown parameters in ODE models based on experimental data. Identifiability analysis is the first step in determing unknown parameters in ODE models and such analysis techniques for nonlinear ODE models are still under development. In this article, we review identifiability analysis methodologies for nonlinear ODE models developed in the past one to two decades, including structural identifiability analysis, practical identifiability analysis and sensitivity-based identifiability analysis. Some advanced topics and ongoing research are also briefly reviewed. Finally, some examples from modeling viral dynamics of HIV, influenza and hepatitis viruses are given to illustrate how to apply these identifiability analysis methods in practice." @default.
• W2113105229 created "2016-06-24" @default.
• W2113105229 creator A5045155778 @default.
• W2113105229 creator A5058338534 @default.
• W2113105229 creator A5085827512 @default.
• W2113105229 creator A5091665121 @default.
• W2113105229 date "2011-01-01" @default.
• W2113105229 modified "2023-10-14" @default.
• W2113105229 title "On Identifiability of Nonlinear ODE Models and Applications in Viral Dynamics" @default.
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• W2113105229 doi "https://doi.org/10.1137/090757009" @default.
• W2113105229 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3140286" @default.
• W2113105229 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/21785515" @default.
• W2113105229 hasPublicationYear "2011" @default.
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