ArticleBulletin of mathematical biology2019
A Parameter Estimation Method for Multiscale Models of Hepatitis C Virus Dynamics.
Article in Bulletin of mathematical biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Review
- Advances in Parameter Estimation and Learning from Data for Mathematical Models of Hepatitis C Viral Kinetics.Mathematics (Basel, Switzerland) · 2022Article
- Machine learning for mathematical models of HCV kinetics during antiviral therapy.Mathematical biosciences · 2022Article
- Modeling-Based Response-Guided Glecaprevir-Pibrentasvir Therapy for Chronic Hepatitis C to Identify Patients for Ultrashort Treatment Duration.The Journal of infectious diseases · 2020Article
- Efficient Methods for Parameter Estimation of Ordinary and Partial Differential Equation Models of Viral Hepatitis Kinetics.Mathematics (Basel, Switzerland) · 2020Article
- Modeling based response guided therapy in subjects with recent hepatitis C infection.Antiviral research · 2020Article
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Authors and funding
5 authors.
Funding
Abstract
Mathematical models that are based on differential equations require detailed knowledge about the parameters that are included in the equations. Some of the parameters can be measured experimentally while others need to be estimated. When the models become more sophisticated, such as in the case of multiscale models of hepatitis C virus dynamics that deal with partial differential equations (PDEs), several strategies can be tried. It is possible to use parameter estimation on an analytical approximation of the solution to the multiscale model equations, namely the long-term approximation, but this limits the scope of the parameter estimation method used and a long-term approximation needs to be derived for each model. It is possible to transform the PDE multiscale model to a system of ODEs, but this has an effect on the model parameters themselves and the transformation can become problematic for some models. Finally, it is possible to use numerical solutions for the multiscale model and then use canned methods for the parameter estimation, but the latter is making the user dependent on a black box without having full control over the method. The strategy developed here is to start by working directly on the multiscale model equations for preparing them toward the parameter estimation method that is fully coded and controlled by the user. It can also be adapted to multiscale models of other viruses. The new method is described, and illustrations are provided using a user-friendly simulator that incorporates the method.
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