Towards Real Time Epidemiology: Data Assimilation, Modeling and Anomaly Detection of Health Surveillance Data Streams
Towards Real Time Epidemiology: Data Assimilation, Modeling and Anomaly Detection of Health Surveillance Data Streams
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迈向实时流行病学：健康监测数据流的数据同化、建模和异常检测
DOI：
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发表时间：
2007
期刊：
影响因子：
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通讯作者：
C. Castillo
中科院分区：
文献类型：
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作者：
L. Bettencourt;R. Ribeiro;G. Chowell;T. Lant;C. Castillo
介绍了一种综合的定量方法，用于实时公共卫生监测数据流的数据同化、预测和异常检测。强调了建立能够从过去和现在的疾病发病率数据预测未来新病例的疾病动力学的动态概率模型的重要性。实时数据同化的方法，它依赖于概率公式和贝叶斯定理之间的概率密度转换为新的情况下，模型参数的开发。这一提法创造了未来的前景与量化的不确定性，并导致自然异常检测方案，量化和检测疾病的演变或人口结构的变化。最后，这些方法和伴随的干预工具在真实的时间公共卫生情况下的实施是通过它们嵌入在最先进的信息技术和交互式可视化环境中来实现的。
An integrated quantitative approach to data assimilation, prediction and anomaly detection over real-time public health surveillance data streams is introduced. The importance of creating dynamical probabilistic models of disease dynamics capable of predicting future new cases from past and present disease incidence data is emphasized. Methods for real-time data assimilation, which rely on probabilistic formulations and on Bayes’ theorem to translate between probability densities for new cases and for model parameters are developed. This formulation creates future outlook with quantified uncertainty, and leads to natural anomaly detection schemes that quantify and detect disease evolution or population structure changes. Finally, the implementation of these methods and accompanying intervention tools in real time public health situations is realized through their embedding in state of the art information technology and interactive visualization environments.