Nowcasting influenza epidemics using non-homogeneous hidden Markov models.

Timeliness of a public health surveillance system is one of its most important characteristics. The process of predicting the present situation using available incomplete information from surveillance systems has received the term nowcasting and has high public health interest. Generally in Europe, general practitioners' sentinel networks support the epidemiological surveillance of influenza activity, and each week's epidemiological bulletins are usually issued between Wednesday and Friday of the following week. In this work, we have developed a non-homogeneous hidden Markov model (HMM) that, on a weekly basis, uses as covariates an early observation of influenza-like illness (ILI) incidence rate and the number of ILI cases tested positive to nowcast the current week ILI rate and the probability that the influenza activity is in an epidemic state. We use Bayesian inference to find estimates of the model parameters and nowcasted quantities. The results obtained with data provided by the Portuguese influenza surveillance system show the additional value of using a non-homogeneous HMM instead of a homogeneous one. The use of a non-homogeneous HMM improves the surveillance system timeliness in 2 weeks.