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

Abstract

S yndromic surveillance uses prediagnostic healthrelated data to signal probable outbreaks warranting public health response (1). Alerta and Vigila are internet-based syndromic surveillance systems successively implemented by Peru's navy (2-4). Among other disease syndromes, individual cases of acute diarrheal disease (ADD) are self-reported to healthcare workers at sites providing care for service members, dependents, and civilian employees. We assessed the performance of 3 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 (5-9).