Mathematical Analysis and Dynamic Active Subspaces for a Long term model of HIV
For researchers studying HIV dynamics, this provides a computationally cheaper approximation of T-cell counts, though the approach is incremental.
The paper analyzes a long-term HIV model by determining infection-free steady states and local stability, then uses active subspace methods for global sensitivity analysis and creates reduced-order models for inexpensive computation of T-cell count dynamics.
Recently, a long-term model of HIV infection dynamics was developed to describe the entire time course of the disease. It consists of a large system of ODEs with many parameters, and is expensive to simulate. In the current paper, this model is analyzed by determining all infection-free steady states and studying the local stability properties of the unique biologically-relevant equilibrium. Active subspace methods are then used 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.