摘要：This paper reviews di.erent approaches for determining the epidemic period from in.uenza surveillance data. In the first approach, the process of di.erenced incidence rates is mo deled either with a first-order autoregressive pro cess or with a Gaussian white noise process depending on whether the system is in an epidemic or a non- epidemic phase. The second approach allows us to directly model the pro cess of the observed cases via a Bayesian hierarchical Poisson model with Gaussian incidence rates whose parameters are mo deled di.erently, depending on the epidemic phase of the system. In both cases transitions between both phases are modeled with a hidden Markov switching model over the epidemic state. Bayesian inference is carried out and both mo dels provide the probability of being in epidemic state at any given moment. A comparison of b oth metho dologies with previous approaches in terms of sensitivity, specificity and timeliness is also performed. Finally, we also review a web-based client application which implements the first methodology for obtaining the posterior probability of b eing in an epidemic phase.
