A kernel-based spatio-temporal surveillance system for monitoring influenza-like illness incidence

Abstract

The threat of pandemics has made influenza surveillance systems a priority in epidemiology services around the world. The emergence of A-H1N1 influenza has required accurate surveillance systems in order to undertake specific actions only when and where they are necessary. In that sense, the main goal of this article is to describe a novel methodology for monitoring the geographical distribution of the incidence of influenza-like illness, as a proxy for influenza, based on information from sentinel networks. A Bayesian Poisson mixed linear model is proposed in order to describe the observed cases of influenza-like illness for every sentinel and week of surveillance. This model includes a spatio-temporal random effect that shares information in space by means of a kernel convolution process and in time by means of a first order autoregressive process. The extrapolation of this term to sites where information on incidence is not available will allow us to visualise the geographical distribution of the disease for every week of study. The following article shows the performance of this model in the Comunitat Valenciana's Sentinel Network (one of the 17 autonomous regions of Spain) as a real case study of this methodology.