Evaluating the ALERT algorithm for local outbreak onset detection in seasonal infectious disease surveillance data. (27th January 2020)

• Record Type: Journal Article
• Authors: Brown, Alexandria C.; Lauer, Stephen A.; Robinson, Christine C.; Nyquist, Ann‐Christine; Rao, Suchitra; Reich, Nicholas G.
• Is Part Of: Statistics in medicine. Volume 39:Number 8(2020)
• Page Start: 1145
• Page End: 1155
• Publication Date: 2020-01-27
• Subjects: hospital epidemiology -- infection control -- influenza -- outbreak detection -- surveillance; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8467
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : 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.