Abstract:
Ordinary differential equations (ODEs) are a powerful tool for modeling dynamic processes
with wide applications in a variety of scientific fields. Over the last two 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 determining 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 couple of 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 and influenza viruses are given to illustrate how to apply these identifiability analysis
methods in practice.
