Centro Superior de Investigación en Salud Pública, Valencia, Spain.
Stat Methods Med Res. 2011 Apr;20(2):103-18. doi: 10.1177/0962280210370265. Epub 2010 Jun 2.
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.
大流行的威胁使得流感监测系统成为世界各地流行病学服务的重点。甲型 H1N1 流感的出现要求建立准确的监测系统，以便在必要时和必要的地方采取具体行动。从这个意义上说，本文的主要目的是描述一种新的方法，用于监测流感样疾病（作为流感的替代指标）的地理分布，该方法基于哨点网络的信息。提出了贝叶斯泊松混合线性模型，以便描述每个哨点和监测周的流感样疾病的观察病例。该模型包括一个时空随机效应，通过核卷积过程在空间上共享信息，通过一阶自回归过程在时间上共享信息。将这一项外推到发病率信息不可用的地点，将使我们能够可视化研究期间每一周疾病的地理分布。本文以下部分将展示该模型在西班牙瓦伦西亚社区（西班牙 17 个自治区之一）的哨点网络中的性能，作为该方法的实际案例研究。
