Our group testing framework describes an interaction between a testing environment, the wet_lab, whose pooled test results are used by the sampler to draw thousands of plausible hypotheses on the infection status of all individuals. These hypotheses are then used by an optimization procedure, group_selector, that figures out what groups may be the most relevant to test in order to narrow down on the true infection status. Once formed, these new groups are then tested again, closing the loop. At any point in the procedure, the hypotheses formed by the sampler can be averaged to obtain the average probability of infection for each patient. From these probabilities, a decision on whether a patient is infected or not can be done by thresholding these probabilities at a certain confidence level. |
