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Dynamic Active Subspaces for a Long Term Model of HIV

Graduate #72
Discipline: Mathematics and Statistics
Subcategory: Mathematics and Statistics

Tyson Loudon - University of Minnesota
Co-Author(s): Stephen Pankavich, Colorado School of Mines, Golden, CO.



The Human Immunodeficiency Virus (HIV) disables many components of the body’s immune system, and without antiretroviral treatment, leads to the onset of Acquired Immune Deficiency Syndrome (AIDS) and subsequently death. The infection progresses through three stages: initial or acute infection, an asymptomatic or latent period, and finally AIDS. Modeling the entire time course of HIV within the body can be difficult as many models have oversimplified its biological dynamics in the effort to gain mathematical insight, but fail to capture all three stages of infection. Only one HIV model has been able to describe the entire time course of the infection, but this is a large system of ODEs with many parameters and is expensive to simulate. In this presentation I’ll describe how we have used active subspace methods to perform a global sensitivity analysis and study the dependence of an infected individual’s T-cell count on the parameter space. Building on these results, a global-in-time approximation of the T-cell count is created by constructing dynamic active subspaces and reduced order models are generated, thereby allowing for inexpensive computation.

Not Submitted

Funder Acknowledgement(s): This research is supported by the US National Science Foundation under award DMS 12-11667.

Faculty Advisor: Stephen Pankavich, pankavic@mines.edu

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This material is based upon work supported by the National Science Foundation (NSF) under Grant No. DUE-1930047. Any opinions, findings, interpretations, conclusions or recommendations expressed in this material are those of its authors and do not represent the views of the AAAS Board of Directors, the Council of AAAS, AAAS’ membership or the National Science Foundation.

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