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首页> 外文期刊>BMC Medical Informatics and Decision Making >Disease surveillance using a hidden Markov model
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Disease surveillance using a hidden Markov model

机译：使用隐马尔可夫模型进行疾病监测

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Background Routine surveillance of disease notification data can enable the early detection of localised disease outbreaks. Although hidden Markov models (HMMs) have been recognised as an appropriate method to model disease surveillance data, they have been rarely applied in public health practice. We aimed to develop and evaluate a simple flexible HMM for disease surveillance which is suitable for use with sparse small area count data and requires little baseline data. Methods A Bayesian HMM was designed to monitor routinely collected notifiable disease data that are aggregated by residential postcode. Semi-synthetic data were used to evaluate the algorithm and compare outbreak detection performance with the established Early Aberration Reporting System (EARS) algorithms and a negative binomial cusum. Results Algorithm performance varied according to the desired false alarm rate for surveillance. At false alarm rates around 0.05, the cusum-based algorithms provided the best overall outbreak detection performance, having similar sensitivity to the HMMs and a shorter average time to detection. At false alarm rates around 0.01, the HMM algorithms provided the best overall outbreak detection performance, having higher sensitivity than the cusum-based Methods and a generally shorter time to detection for larger outbreaks. Overall, the 14-day HMM had a significantly greater area under the receiver operator characteristic curve than the EARS C3 and 7-day negative binomial cusum algorithms. Conclusion Our findings suggest that the HMM provides an effective method for the surveillance of sparse small area notifiable disease data at low false alarm rates. Further investigations are required to evaluation algorithm performance across other diseases and surveillance contexts.
机译：背景疾病报告数据的例行监视可以使早期发现局部疾病暴发。尽管隐马尔可夫模型（HMM）已被认为是对疾病监测数据进行建模的合适方法，但很少在公共卫生实践中应用。我们旨在开发和评估一种用于疾病监测的简单灵活的HMM，适用于稀疏的小面积计数数据，并且几乎不需要基线数据。方法设计贝叶斯HMM来监视常规收集的应报告疾病数据，这些数据通过居住区邮政编码汇总。使用半合成数据评估算法，并将爆发检测性能与已建立的早期像差报告系统（EARS）算法和负二项式cusum比较。结果算法性能根据监视所需的误报率而变化。在误报率大约为0.05时，基于cusum的算法提供了最佳的总体爆发检测性能，对HMM的敏感性相似，并且平均检测时间更短。在误报率大约为0.01的情况下，HMM算法提供了最佳的总体爆发检测性能，其灵敏度高于基于cusum的方法，并且通常更短的时间检测出较大的爆发。总体而言，与EARS C3和7天负二项式cusum算法相比，14天HMM在接收者操作员特征曲线下的面积明显更大。结论我们的发现表明，HMM提供了一种有效的方法，以较低的误报率监视稀疏的小区域可报告疾病数据。需要进一步研究以评估算法在其他疾病和监视环境中的性能。

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