Approximate-model closed-loop minimal sampling method for HIV viral-load minima detection

We present a closed-loop method for determining the minimum of a viral rebound curve for an HIV patient undergoing a therapy transition. This method fits the parameters for a reduced approximate solution to the viral load measurements using a Simulated Annealing direct search algorithm. Gaussian white noise is added, and a family of fits is obtained. A safety tolerance measure is applied to the family of fits to obtain the next sample time. Using parameters identified from HIV data, we show that this method exhibits robust performance on noisy data generated from identified patient models, while greatly reducing the number of samples needed compared to a fixed-interval sampling approach.