Assessing 3 Outbreak Detection Algorithms in an Electronic Syndromic Surveillance System in a Resource-Limited Setting

Emily Alsentzer,Sarah-Blythe Ballard,Jose Quispe,Delphis M Vera,Joan Neyra,David L Blazes,Luis Loayza,Victor B Osorio

doi:10.3201/eid09.191315

Abstract

Assessing 3 Outbreak Detection Algorithms in an Electronic Syndromic Surveillance System in a Resource-Limited Setting

Highlights

We evaluated the performance of X-bar chart, exponentially weighted moving average, and C3 cumulative sums aberration detection algorithms for acute diarrheal disease syndromic surveillance at naval sites in Peru during 2007–2011
We assessed the performance of 3 acute diarrheal disease (ADD) aberration detection algorithms in this resource-limited setting: X-bar chart, exponentially weighted moving average (EWMA), and Early Aberration Reporting System (EARS) C3 cumulative sums (CUSUM) models [5,6,7,8,9]
87% occurred in persons >5 years of age, 9% in children 1–4 years of age, and 4% in children

Summary

Conclusions

X-bar, EWMA, and C3 CUSUM aberration detection algorithms identified all ADD outbreaks during 2009–2011, and approximately one third to one half of algorithm outbreak signals corresponded to true outbreaks. These findings suggest that these algorithms can usefully inform outbreak asset deployment, in resource-limited settings. CUSUM frequently produced false-positives in the weeks after large outbreaks (e.g., after the 76-case outbreak at Policlínico Naval Ancón) (Table). Nonbloody acute diarrheal disease case count and incidence, 2007–2011, and true outbreak detection data and algorithm performance, 2009–2011, for the 45 naval surveillance sites in Peru, analyzed by using X-bar chart, exponentially weighted moving average, and Early Aberration Reporting System C3 cumulative sums models*

Posta Naval de Ventanilla

Findings

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