Andersson Eva, Bock David, Frisén Marianne
Department of Economics, Göteborg University, Göteborg, Sweden.
Stat Methods Med Res. 2008 Aug;17(4):421-38. doi: 10.1177/0962280206078986. Epub 2007 Aug 14.
We describe and discuss statistical models of Swedish influenza data, with special focus on aspects which are important in on-line monitoring. Earlier suggested statistical models are reviewed and the possibility of using them to describe the variation in influenza-like illness (ILI) and laboratory diagnoses (LDI) is discussed. Exponential functions were found to work better than earlier suggested models for describing the influenza incidence. However, the parameters of the estimated functions varied considerably between years. For monitoring purposes we need models which focus on stable indicators of the change at the outbreak and at the peak.For outbreak detection we focus on ILI data. Instead of a parametric estimate of the baseline (which could be very uncertain), we suggest a model utilizing the monotonicity property of a rise in the incidence. For ILI data at the outbreak, Poisson distributions can be used as a first approximation.To confirm that the peak has occurred and the decline has started, we focus on LDI data. A Gaussian distribution is a reasonable approximation near the peak. In view of the variability of the shape of the peak, we suggest that a detection system use the monotonicity properties of a peak.
我们描述并讨论了瑞典流感数据的统计模型，特别关注在线监测中重要的方面。回顾了早期提出的统计模型，并讨论了使用它们来描述流感样疾病（ILI）和实验室诊断（LDI）变化的可能性。发现指数函数在描述流感发病率方面比早期提出的模型效果更好。然而，估计函数的参数在不同年份之间有很大差异。出于监测目的，我们需要关注疫情爆发和高峰期变化稳定指标的模型。对于疫情检测，我们关注ILI数据。我们建议使用一个利用发病率上升的单调性的模型，而不是对基线进行参数估计（这可能非常不确定）。对于疫情爆发时的ILI数据，泊松分布可以作为一阶近似。为了确认高峰期已经出现且下降已经开始，我们关注LDI数据。高斯分布在高峰期附近是一个合理的近似。鉴于高峰期形状的变异性，我们建议检测系统利用峰值的单调性。
