Centre for Quantitative Medicine, Office of Clinical Sciences, Duke-NUS Graduate Medical School Singapore, Singapore 169857.
Comput Math Methods Med. 2012;2012:758674. doi: 10.1155/2012/758674. Epub 2012 Mar 8.
Dengue fever (DF) is a serious public health problem in many parts of the world, and, in the absence of a vaccine, disease surveillance and mosquito vector eradication are important in controlling the spread of the disease. DF is primarily transmitted by the female Aedes aegypti mosquito. We compared two statistical models that can be used in the surveillance and forecast of notifiable infectious diseases, namely, the Autoregressive Integrated Moving Average (ARIMA) model and the Knorr-Held two-component (K-H) model. The Mean Absolute Percentage Error (MAPE) was used to compare models. We developed the models using used data on DF notifications in Singapore from January 2001 till December 2006 and then validated the models with data from January 2007 till June 2008. The K-H model resulted in a slightly lower MAPE value of 17.21 as compared to the ARIMA model. We conclude that the models' performances are similar, but we found that the K-H model was relatively more difficult to fit in terms of the specification of the prior parameters and the relatively longer time taken to run the models.
登革热（DF）是世界上许多地区的一个严重公共卫生问题，并且在没有疫苗的情况下，疾病监测和蚊虫媒介根除对于控制疾病的传播非常重要。DF 主要由雌性埃及伊蚊传播。我们比较了两种可用于监测和预测法定传染病的统计模型，即自回归综合移动平均（ARIMA）模型和 Knorr-Held 两分量（K-H）模型。我们使用了新加坡 2001 年 1 月至 2006 年 12 月期间的登革热通知数据来开发模型，然后使用 2007 年 1 月至 2008 年 6 月的数据验证模型。K-H 模型的平均绝对百分比误差（MAPE）值略低，为 17.21，而 ARIMA 模型的 MAPE 值为 17.59。我们得出结论，两个模型的性能相似，但我们发现 K-H 模型在指定先验参数方面相对较难拟合，并且运行模型所需的时间相对较长。
