Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, Massachusetts.
Department of Pediatrics, Section of Infectious Diseases and Epidemiology, Department of Epidemiology, University of Colorado School of Medicine and Children's Hospital Colorado, Aurora, Colorado.
Stat Med. 2020 Apr 15;39(8):1145-1155. doi: 10.1002/sim.8467. Epub 2020 Jan 27.
Estimation of epidemic onset timing is an important component of controlling the spread of seasonal infectious diseases within community healthcare sites. The Above Local Elevated Respiratory Illness Threshold (ALERT) algorithm uses a threshold-based approach to suggest incidence levels that historically have indicated the transition from endemic to epidemic activity. In this paper, we present the first detailed overview of the computational approach underlying the algorithm. In the motivating example section, we evaluate the performance of ALERT in determining the onset of increased respiratory virus incidence using laboratory testing data from the Children's Hospital of Colorado. At a threshold of 10 cases per week, ALERT-selected intervention periods performed better than the observed hospital site periods (2004/2005-2012/2013) and a CUSUM method. Additional simulation studies show how data properties may effect ALERT performance on novel data. We found that the conditions under which ALERT showed ideal performance generally included high seasonality and low off-season incidence.
估算疫情爆发时间是控制社区医疗机构内季节性传染病传播的重要组成部分。Above Local Elevated Respiratory Illness Threshold（ALERT）算法使用基于阈值的方法来提示历史上表明从地方病到流行活动转变的发病率水平。在本文中，我们首次详细介绍了该算法背后的计算方法。在激励示例部分，我们使用科罗拉多儿童医院的实验室检测数据评估了 ALERT 确定呼吸道病毒发病率上升的起始时间的性能。在每周 10 例的阈值下，ALERT 选择的干预期的表现优于观察到的医院现场期（2004/2005-2012/2013）和 CUSUM 方法。其他模拟研究表明，数据特性如何影响 ALERT 在新数据上的性能。我们发现，ALERT 表现理想的条件通常包括高季节性和低淡季发病率。
