Early detection of influenza outbreaks is a challenging issue in disease surveillance. There have been several proposals for triggering the alert of outbreak as soon and accurate as possible. One approach are the Markov Switching Models, where a latent variable has two possible values representing the epidemic and non-epidemic states for each time (an location for spatio-temporal models), and two possible models with different structures are selected according to the value of the latent variable. Martinez-Beneito et al. 2008 proposed a Markov Switching Model for the detection of influenza outbreaks where the observations where the differentiated rates. This helps distinguishing the epidemic state even in low rates. Given that influenza dispersion is related to climate variables and spreads person to person, a spatio-temporal extension of this model is a natural improvement where data from nearby locations helps detect the epidemic state. The spatial and temporal relation may be modeled through Gaussian Markov random fields. Bayesian paradigm allows to easily estimate the posterior distribution of all the parameters of the model. In particular the posterior distribution of the latent variables of the Markov Switching Model is the tool of decision for assessing the risk of epidemic. |