Dynamic linear model and SARIMA: a comparison of their forecasting performance in epidemiology
Dynamic linear model and SARIMA: a comparison of their forecasting performance in epidemiology
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DOI：
10.1002/sim.963
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发表时间：
2001-10-30
影响因子：
2
通讯作者：
Williamson, GD
中科院分区：
文献类型：
--
作者：
Nobre, FF;Monteiro, ABS;Williamson, GD
公共卫生监测系统的一个目标是提供流行病学时间序列的可靠预测。本文介绍了一项研究，使用收集的数据，通过在美国的国家公共卫生监测系统，以评估和比较的性能的季节性自回归综合移动平均（SARIMA）和动态线性模型（DLM）估计两种法定报告疾病的病例发生率。该比较使用了美国1980年1月至1995年6月报告的疟疾和甲型肝炎病例。这两个预测模型的残差表明，它们是足够的工具，用于流行病学监测。考虑了两种模式的定性方面，以改进它们在公共卫生方面的实用性的比较。我们的比较发现，两种预测建模技术（SARIMA和DLM）是可比的，当长期的历史数据（至少52个报告期）。然而，DLM方法有一些优点，例如更容易应用于不同类型的时间序列，并且在获得新数据时不需要新的识别和建模周期。版权所有（C）2001约翰威利父子有限公司
One goal of a public health surveillance system is to provide a reliable forecast of epidemiological time series. This paper describes a study that used data collected through a national public health surveillance system in the United States to evaluate and compare the performances of a seasonal autoregressive integrated moving average (SARIMA) and a dynamic linear model (DLM) for estimating case occurrence of two notifiable diseases. The comparison uses reported cases of malaria and hepatitis A from January 1980 to June 1995 for the United States. The residuals for both predictor models show that they were adequate tools for use in epidemiological surveillance. Qualitative aspects were considered for both models to improve the comparison of their usefulness in public health. Our comparison found that the two forecasting modelling techniques (SARIMA and DLM) are comparable when long historical data are available (at least 52 reporting periods). However, the DLM approach has some advantages, such as being more easily applied to different types of time series and not requiring a new cycle of identification and modelling when new data become available. Copyright (C) 2001 John Wiley & Sons, Ltd.