Methods for monitoring influenza surveillance data
Methods for monitoring influenza surveillance data
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DOI：
10.1093/ije/dyl162
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发表时间：
2006-10-01
影响因子：
7.7
通讯作者：
Leung, Gabriel M.
中科院分区：
文献类型：
--
作者：
Cowling, Benjamin J.;Wong, Irene O. L.;Leung, Gabriel M.
背景各种Serfling类型的统计算法，需要长系列的历史数据，专门从温带气候区，已被提出用于自动监测流感哨点监测数据。我们评估了三种替代的统计方法，其中警报阈值是基于最近的数据在温带和亚热带regions.Methods我们比较了时间序列，回归和累积和（CIMUM）模型的经验数据从香港和美国使用的复合指数（范围= 0-1）组成的敏感性，特异性和检测时间（滞后）的关键成果。该指数计算的基础上产生的警报在第一个2或4周内的旺季。结果我们发现，时间序列模型是最佳的香港设置，而时间序列和CANUUM模型同样适用于美国的数据。对于在香港旺季的前2周（4周）内产生的警报，指数的最大值为：时间序列0.77（0.86）;回归0.75（0.82）;最大值0.56（0.75）。在美国的数据中，该指数的最大值为：时间序列0. 81（0.95）;回归0.81（0.91）;单位：0.90结论基于短期数据的自动流感监测方法，包括时间序列和CANUUM模型，可以产生敏感、特异和及时的警报，并且可以为依赖于长期的、基于历史的阈值的Serfling类方法提供有用的替代方案。
Background A variety of Serfling-type statistical algorithms requiring long series of historical data, exclusively from temperate climate zones, have been proposed for automated monitoring of influenza sentinel surveillance data. We evaluated three alternative statistical approaches where alert thresholds are based on recent data in both temperate and subtropical regions.Methods We compared time series, regression, and cumulative sum (CUSUM) models on empirical data from Hong Kong and the US using a composite index (range = 0-1) consisting of the key outcomes of sensitivity, specificity, and time to detection (lag). The index was calculated based on alarms generated within the first 2 or 4 weeks of the peak season.Results We found that the time series model was optimal in the Hong Kong setting, while both the time series and CUSUM models worked equally well on US data. For alarms generated within the first 2 weeks (4 weeks) of the peak season in Hong Kong, the maximum values of the index were: time series 0.77 (0.86); regression 0.75 (0.82); CUSUM 0.56 (0.75). In the US data the maximum values of the index were: time series 0.81 (0.95); regression 0.81 (0.91); CUSUM 0.90 (0.94).Conclusions Automated influenza surveillance methods based on short-term data, including time series and CUSUM models, can generate sensitive, specific, and timely alerts, and can offer a useful alternative to Serfling-like methods that rely on long-term, historically based thresholds.