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Evaluating the ALERT algorithm for local outbreak onset detection in seasonal infectious disease surveillance data

Alexandria C. Brown, View ORCID ProfileStephen A. Lauer, Christine C. Robinson, Ann-Christine Nyquist, Suchitra Rao, View ORCID ProfileNicholas G. Reich
doi: https://doi.org/10.1101/664433
Alexandria C. Brown
1Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, USA
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Stephen A. Lauer
1Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, USA
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Christine C. Robinson
2Department of Pediatrics, Section of Infectious Diseases and Epidemiology, Department of Epidemiology, University of Colorado School of Medicine and Children’s Hospital Colorado, Aurora, CO, USA
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Ann-Christine Nyquist
2Department of Pediatrics, Section of Infectious Diseases and Epidemiology, Department of Epidemiology, University of Colorado School of Medicine and Children’s Hospital Colorado, Aurora, CO, USA
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Suchitra Rao
3Pediatric Infectious Diseases/Hospital Medicine/Epidemiology, Children’s Hospital Colorado and University of Colorado, Aurora, CO, USA
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Nicholas G. Reich
1Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, USA
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  • For correspondence: nick@umass.edu
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Abstract

Estimation of epidemic onset timing is an important component of controlling the spread of seasonal infectious dis-eases 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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted June 14, 2019.
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Evaluating the ALERT algorithm for local outbreak onset detection in seasonal infectious disease surveillance data
Alexandria C. Brown, Stephen A. Lauer, Christine C. Robinson, Ann-Christine Nyquist, Suchitra Rao, Nicholas G. Reich
bioRxiv 664433; doi: https://doi.org/10.1101/664433
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Evaluating the ALERT algorithm for local outbreak onset detection in seasonal infectious disease surveillance data
Alexandria C. Brown, Stephen A. Lauer, Christine C. Robinson, Ann-Christine Nyquist, Suchitra Rao, Nicholas G. Reich
bioRxiv 664433; doi: https://doi.org/10.1101/664433

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