Bayesian Nowcasting during the STEC O104:H4 Outbreak in Germany, 2011
Bayesian Nowcasting during the STEC O104:H4 Outbreak in Germany, 2011
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DOI：
10.1111/biom.12194
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发表时间：
2014-12-01
期刊：
影响因子：
1.9
通讯作者：
an der Heiden, Matthias
中科院分区：
文献类型：
--
作者：
Hohle, Michael;an der Heiden, Matthias
贝叶斯方法预测已发生但尚未报告的事件被开发用于实时公共卫生监测。研究的动机是预测2011年5月至7月德国产志贺毒素大肠杆菌O104:H4大规模暴发期间因溶血性尿毒症综合征而住院的每日人数。我们的新贝叶斯方法使用负二项抽样解决了问题的计数数据性质，并表明在时间均匀性假设下报告延迟分布的右截断可以在使用广义狄利克雷分布的共轭先验-后验框架中处理。回顾过去，由于可以获得真实的住院人数，因此可以使用计数数据的适当评分规则来评估和比较疫情期间程序的预测质量。结果表明，考虑时间序列的计数性质和由于干预措施而发生的延迟分布变化是重要的。因此，我们将贝叶斯分析扩展到一个层次模型，该模型结合了用于延迟分布的离散时间生存回归模型和用于流行病曲线动力学的惩罚样条。总之，我们得出结论，在新出现的和时间紧迫的疫情中，临近预报方法是获得有关当前趋势信息的宝贵工具。
A Bayesian approach to the prediction of occurred-but-not-yet-reported events is developed for application in real-time public health surveillance. The motivation was the prediction of the daily number of hospitalizations for the hemolytic-uremic syndrome during the large May-July 2011 outbreak of Shiga toxin-producing Escherichia coli (STEC) O104:H4 in Germany. Our novel Bayesian approach addresses the count data nature of the problem using negative binomial sampling and shows that right-truncation of the reporting delay distribution under an assumption of time-homogeneity can be handled in a conjugate prior-posterior framework using the generalized Dirichlet distribution. Since, in retrospect, the true number of hospitalizations is available, proper scoring rules for count data are used to evaluate and compare the predictive quality of the procedures during the outbreak. The results show that it is important to take the count nature of the time series into account and that changes in the delay distribution occurred due to intervention measures. As a consequence, we extend the Bayesian analysis to a hierarchical model, which combines a discrete time survival regression model for the delay distribution with a penalized spline for the dynamics of the epidemic curve. Altogether, we conclude that in emerging and time-critical outbreaks, nowcasting approaches are a valuable tool to gain information about current trends.