Prospective analysis of infectious disease surveillance data using syndromic information
Prospective analysis of infectious disease surveillance data using syndromic information
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DOI：
10.1177/0962280214527385
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发表时间：
2014-12-01
影响因子：
2.3
通讯作者：
Lawson, Andrew B.
中科院分区：
文献类型：
--
作者：
Corberan-Vallet, Ana;Lawson, Andrew B.
在本文中，我们描述了一个贝叶斯分层泊松模型的前瞻性分析数据的传染病。所提出的模式包括两个组成部分。第一个组成部分描述了疾病在非流行期间的行为，第二个组成部分表示由于流行病的存在而导致的疾病数量的增加。我们的模型制定的一个新奇是，描述流行病的传播的参数被允许在空间和时间上变化。我们还展示了如何将症状信息纳入模型，以提供更好的数据描述和更准确的一步预测。这些实时预测可用于确定爆发的高风险地区，从而制定有效的有针对性的监测。我们将此方法应用于南卡罗来纳州急性支气管炎的每周急诊室出院病例。
In this paper, we describe a Bayesian hierarchical Poisson model for the prospective analysis of data for infectious diseases. The proposed model consists of two components. The first component describes the behavior of disease during nonepidemic periods and the second component represents the increase in disease counts due to the presence of an epidemic. A novelty of our model formulation is that the parameters describing the spread of epidemics are allowed to vary in both space and time. We also show how syndromic information can be incorporated into the model to provide a better description of the data and more accurate one-step-ahead forecasts. These real-time forecasts can be used to identify high-risk areas for outbreaks and, consequently, to develop efficient targeted surveillance. We apply the methodology to weekly emergency room discharges for acute bronchitis in South Carolina.