Applied Physics Laboratory, The Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD 20723, USA.
Stat Med. 2009 Nov 20;28(26):3226-48. doi: 10.1002/sim.3708.
This paper discusses further advances in making robust predictions with the Holt-Winters forecasts for a variety of syndromic time series behaviors and introduces a control-chart detection approach based on these forecasts. Using three collections of time series data, we compare biosurveillance alerting methods with quantified measures of forecast agreement, signal sensitivity, and time-to-detect. The study presents practical rules for initialization and parameterization of biosurveillance time series. Several outbreak scenarios are used for detection comparison. We derive an alerting algorithm from forecasts using Holt-Winters-generalized smoothing for prospective application to daily syndromic time series. The derived algorithm is compared with simple control-chart adaptations and to more computationally intensive regression modeling methods. The comparisons are conducted on background data from both authentic and simulated data streams. Both types of background data include time series that vary widely by both mean value and cyclic or seasonal behavior. Plausible, simulated signals are added to the background data for detection performance testing at signal strengths calculated to be neither too easy nor too hard to separate the compared methods. Results show that both the sensitivity and the timeliness of the Holt-Winters-based algorithm proved to be comparable or superior to that of the more traditional prediction methods used for syndromic surveillance.
本文进一步探讨了使用 Holt-Winters 预测对各种综合征时间序列行为进行稳健预测的进展，并介绍了一种基于这些预测的控制图检测方法。使用三批时间序列数据，我们比较了生物监测预警方法与预测一致性、信号灵敏度和检测时间的量化度量。该研究提出了生物监测时间序列的初始化和参数化的实用规则。还使用了几种爆发情况进行检测比较。我们从使用 Holt-Winters-广义平滑的预测中推导出一种警报算法，以便将其应用于日常综合征时间序列的前瞻性预测。将所推导的算法与简单的控制图自适应方法和更计算密集的回归建模方法进行了比较。这些比较是在来自真实和模拟数据流的背景数据上进行的。这两种类型的背景数据都包含平均值和周期性或季节性行为差异很大的时间序列。在背景数据中添加了合理的、模拟的信号，以便在计算得出的信号强度下进行检测性能测试，这些信号既不容易也不容易与比较方法区分开来。结果表明，基于 Holt-Winters 的算法的敏感性和及时性都被证明与用于综合征监测的更传统预测方法相当或更优。
