The original problem of group testing consists in the identification of defective items in a collection, by applying tests on groups of items that detect the presence of at least one defective item in the group. The aim is then to identify all defective items of the collection with as few tests as possible. This problem is relevant in several fields, among which biology and computer sciences. It recently gained attraction as a potential tool to solve a shortage of COVID-19 test kits, in particular for RT-qPCR. However, the problem of group testing is not an exact match to this implementation. Indeed, contrarily to the original problem, PCR testing employed for the detection of COVID-19 returns more than a simple binary contaminated/non-contaminated value when applied to a group of samples collected on different individuals. It gives a real value representing the viral load in the sample instead. We study here the use that can be made of this extra piece of information to construct a one-stage pool testing algorithms on an idealize version of this model. We show that under the right conditions, the total number of tests needed to detect contaminated samples diminishes drastically.