The patient-zero problem with noisy observations

A belief propagation approach has been recently proposed for the patient-zero problem in SIR epidemics. The patient-zero problem consists of finding the initial source of an epidemic outbreak given observations at a later time. In this work, we study a more difficult but related inference problem, in which observations are noisy and there is confusion between observed states. In addition to studying the patient-zero problem, we also tackle the problem of completing and correcting the observations to possibly find undiscovered infected individuals and false test results. Moreover, we devise a set of equations, based on the variational expression of the Bethe free energy, to find the patient-zero along with maximum-likelihood epidemic parameters. We show, by means of simulated epidemics, that this method is able to infer details on the past history of an epidemic outbreak based solely on the topology of the contact network and a single snapshot of partial and noisy observations.