Statistical methods to forecast the incidence of influenza in particular, and contagious diseases in general, have changed over time. In one of the first studies on time series, Choi and Thacker  employed an ARIMA model to estimate pneumonia and influenza mortality. Dushoff et al.  used a regression model to investigate how cold temperatures contribute to excess seasonal mortality. Hohle and Paul  proposed an alternative model to monitor infectious diseases that consisted in applying count data charts to monitor time series. From a Bayesian framework, Conesa et al.  an automated monitoring of influenza surveillance data that made it possible to take the geographical component into account in statistical models in addition to temporal evolution was proposed. Contributions to this methodology are growing steadily through disease mapping. The studies by Ugarte et al.  and Paul and Held  are recent examples of this. Their common denominator is that they apply different statistical methodologies to multivariate time series (hierarchical Bayesian space–time, mixed models, P–splines and conditional autoregressive models -CAR-, among others) of infectious disease counts, collected in different geographic areas, using multivariate or longitudinal data.