Sebastiani Paola, Mandl Kenneth D, Szolovits Peter, Kohane Isaac S, Ramoni Marco F
Department of Biostatistics, Boston University, Boston, MA, USA.
Stat Med. 2006 Jun 15;25(11):1803-16; discussion 1817-25. doi: 10.1002/sim.2566.
The severe acute respiratory syndrome (SARS) epidemic, the growing fear of an influenza pandemic and the recent shortage of flu vaccine highlight the need for surveillance systems able to provide early, quantitative predictions of epidemic events. We use dynamic Bayesian networks to discover the interplay among four data sources that are monitored for influenza surveillance. By integrating these different data sources into a dynamic model, we identify in children and infants presenting to the pediatric emergency department with respiratory syndromes an early indicator of impending influenza morbidity and mortality. Our findings show the importance of modelling the complex dynamics of data collected for influenza surveillance, and suggest that dynamic Bayesian networks could be suitable modelling tools for developing epidemic surveillance systems.
严重急性呼吸综合征（SARS）疫情、对流感大流行日益增长的恐惧以及近期流感疫苗的短缺凸显了建立能够对疫情事件提供早期定量预测的监测系统的必要性。我们使用动态贝叶斯网络来发现流感监测所监测的四个数据源之间的相互作用。通过将这些不同的数据源整合到一个动态模型中，我们在因呼吸道综合征到儿科急诊科就诊的儿童和婴儿中识别出即将发生的流感发病和死亡的早期指标。我们的研究结果表明了对流感监测所收集数据的复杂动态进行建模的重要性，并表明动态贝叶斯网络可能是开发疫情监测系统的合适建模工具。
