A time-adaptive optimization approach for reconstructing immune response in a mathematical model of acute HIV infection using clinical data

TL;DR

This paper introduces a novel time-adaptive optimization method for reconstructing immune response functions in a mathematical model of acute HIV infection, leveraging clinical data and adaptive mesh refinement for improved accuracy.

Contribution

It develops a time-adaptive algorithm with error estimates for better immune response reconstruction in HIV models, integrating Tikhonov regularization and adjoint methods.

Findings

The adaptive method outperforms standard methods in accuracy.

Numerical experiments confirm the effectiveness of the approach.

Improved reconstruction of immune response functions during acute HIV infection.

Abstract

The paper proposes a time-adaptive optimization approach for determining the time-dependent immune response function in a mathematical model of acute HIV infection, using clinical data from four untreated patients. We formulate the problem as a parameter identification problem for an immune response system of ODE which includes novel component integrated into the third equation of the classical three-equation HIV model. Tikhonov's regularization method, Lagrangian approach, from which we derive the optimality conditions, and a numerical scheme to solve the forward and adjoint problems, as well as parameter identification problem, are presented. Three different a posteriori error estimates are derived and based on these estimates, a time adaptive optimization algorithm is formulated. Numerical experiments demonstrate the effectiveness of the proposed adaptive method in reconstructing the immune response function during the acute phase of HIV infection, using patient-specific clinical data. Computational results show improvement of reconstruction of immune response function using the local time-adaptive mesh refinement method compared to the standard conjugate gradient method applied on a uniform time mesh.