Figure 2
From: Inference in conditioned dynamics through causality restoration
Area under the ROC (AUC) as a function of the number of observations for the risk assessment problem, i.e. \(t^{\star } = T\), in panel (a), and for the patient-zero problem, \(t^{\star } = 0\), panel (b). The simulated contact graph is a proximity network with average connectivity 2.2/N. For both simulations in panels (a) and (b), the total number of individuals is \(N=50\), the probability of being the zero patient is set to \(\gamma =1/N\), and the infection rate is \(\lambda =0.1\). For each epidemic realization, the inference is performed for an increasing number of noiseless observations (here \(p_{{\rm FNR}}=0\)) at time \(t_{{\rm obs}} = T\). Thick lines and shaded areas indicate the averages and the standard errors computed over 40 different instances.
