Parameter Estimation in Mathematical Models of Viral Infections Using R

Area of Science:

• Virology
• Mathematical Biology
• Computational Biology

Background:

• Mathematical modeling is crucial for understanding viral infectious diseases.
• Parameter estimation for these models is challenging.
• Current methods may not fully leverage computational tools.

Purpose of the Study:

• To present a robust parameter estimation approach for mathematical models of viral dynamics.
• To demonstrate the utility of the R software for this process.
• To enhance experimentalists' ability to analyze viral infection data.

Main Methods:

• Developed a parameter estimation framework using the R statistical software.
• Applied the method to influenza virus dynamics models of varying complexity.
• Included procedures for evaluating the results of parameter estimation.

Main Results:

• The R-based approach facilitates efficient parameter estimation for viral models.
• The method is adaptable to different levels of model complexity.
• Successfully applied to influenza virus dynamics, demonstrating its practical utility.

Conclusions:

• The presented parameter estimation method, implemented in R, offers a valuable tool for researchers studying viral infections.
• This approach can improve the understanding of disease mechanisms and treatment efficacy.
• The methodology is broadly applicable to other viral diseases and biological systems.