Abstract

Rapidly detecting the beginning of influenza outbreaks helps health authorities to reduce their impact. Accounting for the spatial distribution of the data can greatly improve the performance of an outbreak detection method by promptly detecting the first foci of infection. The use of Hidden Markov chains in temporal models has shown to be great tools for classifying the epidemic or endemic state of influenza data, though their use in spatio-temporal models for outbreak detection is scarce. In this work, we present a spatio-temporal Bayesian Markov switching model over the differentiated incidence rates for the rapid detection of influenza outbreaks. This model focuses its attention on the incidence variations to better detect the higher increases of early epidemic rates even when the rates themselves are relatively low. The differentiated rates are modelled by a Gaussian distribution with different mean and variance according to the epidemic or endemic state. A temporal autoregressive term and a spatial conditional autoregressive model are added to capture the spatio-temporal structure of the epidemic mean. The proposed model has been tested over the USA Google Flu Trends database to assess the relevance of the whole structure.

中文翻译：

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时空分层马尔可夫切换模型，用于流感爆发的早期检测

摘要

快速检测流感爆发的开始有助于卫生当局减少其影响。通过迅速检测感染的第一个病灶，考虑到数据的空间分布，可以大大提高爆发检测方法的性能。在时间模型中使用隐马尔可夫链已被证明是对流行性感冒数据的流行或流行状态进行分类的有效工具，尽管在时空模型中用于暴发检测的方法很少。在这项工作中，我们提出了时空贝叶斯马尔科夫切换模型，用于快速检测流感爆发的差异发生率。该模型将注意力集中在发病率变化上，以便即使发病率本身相对较低，也能更好地检测出早期流行率的更高增长。区分率通过高斯分布来建模，该高斯分布具有根据流行或流行状态的不同均值和方差。添加了时间自回归项和空间条件自回归模型以捕获流行病均值的时空结构。建议的模型已在美国Google流感趋势数据库上进行了测试，以评估整个结构的相关性。