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Pairwise Growth Competition Assay for Determining the Replication Fitness of Human Immunodeficiency Viruses

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Uncertainty quantification in modeling HIV viral mechanics.

H T Banks¹, Robert Baraldi, Karissa Cross

¹Center for Research in Scientific Computation, North Carolina State University, Raleigh, NC 27695-8212, United States. htbanks@eos.ncsu.edu.

Mathematical Biosciences and Engineering : MBE

|August 18, 2015

Summary

This study refines an in-host model for human immunodeficiency virus type 1 (HIV-1) infection dynamics. It employs statistical methods and parameter selection techniques to improve accuracy and understand parameter impacts on HIV-1 progression.

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Area of Science:

* Mathematical modeling
* Virology
* Biostatistics

Background:

* Revisiting a previously established in-host model for HIV-1 infection dynamics.
* Incorporating recent advancements in understanding HIV-1 progression in humans.
* Addressing the need for refined statistical descriptions of patient data.

Purpose of the Study:

* To update and enhance an existing HIV-1 in-host model.
* To develop accurate data weighting schemes using statistical models and generalized least squares.
* To investigate the influence of estimated parameters on model selection scores.

Main Methods:

* Application of statistical models to describe HIV-1 infection data.
* Utilization of residual plots within generalized least squares for data weighting.
* Employment of parameter subset selection techniques for impact analysis.
* Comparison of bootstrapping and asymptotic theory for confidence intervals.

Main Results:

* Development of accurate data weighting methods for the HIV-1 model.
* Identification of the impact of specific parameters on model selection.
* Comparative analysis of statistical approaches for parameter estimation.

Conclusions:

* The refined model provides a more accurate description of HIV-1 dynamics.
* Statistical methods enhance the understanding of parameter influence and data weighting.
* The study contributes to improved modeling of viral infections.