Conesa D, Martínez-Beneito M A, Amorós R, López-Quílez A
Departament d'Estadística i Investigació Operativa, Universitat de València, C/ Dr. Moliner 50, 46100 Burjassot (Valencia), Spain.
Centro Superior de Investigación en Salud Pública, Generalitat Valenciana, Av. Cataluña 21, 46021 Valencia, Spain.
Stat Methods Med Res. 2015 Apr;24(2):206-23. doi: 10.1177/0962280211414853. Epub 2011 Aug 25.
Considerable effort has been devoted to the development of statistical algorithms for the automated monitoring of influenza surveillance data. In this article, we introduce a framework of models for the early detection of the onset of an influenza epidemic which is applicable to different kinds of surveillance data. In particular, the process of the observed cases is modelled via a Bayesian Hierarchical Poisson model in which the intensity parameter is a function of the incidence rate. The key point is to consider this incidence rate as a normal distribution in which both parameters (mean and variance) are modelled differently, depending on whether the system is in an epidemic or non-epidemic phase. To do so, we propose a hidden Markov model in which the transition between both phases is modelled as a function of the epidemic state of the previous week. Different options for modelling the rates are described, including the option of modelling the mean at each phase as autoregressive processes of order 0, 1 or 2. Bayesian inference is carried out to provide the probability of being in an epidemic state at any given moment. The methodology is applied to various influenza data sets. The results indicate that our methods outperform previous approaches in terms of sensitivity, specificity and timeliness.
人们在开发用于自动监测流感监测数据的统计算法方面投入了大量精力。在本文中，我们介绍了一个适用于不同类型监测数据的流感疫情早期检测模型框架。具体而言，通过贝叶斯分层泊松模型对观察到的病例过程进行建模，其中强度参数是发病率的函数。关键在于将该发病率视为正态分布，根据系统处于流行或非流行阶段，对其两个参数（均值和方差）进行不同的建模。为此，我们提出了一个隐马尔可夫模型，其中两个阶段之间的转换被建模为前一周流行状态的函数。描述了对发病率进行建模的不同选项，包括将每个阶段的均值建模为0阶、1阶或2阶自回归过程的选项。进行贝叶斯推断以提供在任何给定时刻处于流行状态的概率。该方法应用于各种流感数据集。结果表明，我们的方法在敏感性、特异性和及时性方面优于先前的方法。
