Abstract
The authors describe the challenges of disease surveillance in settings lacking infrastructure and access to medical care. They address the role of analytic methods and evaluate open-source temporal alerting algorithms chosen for the Suite for Automated Global Electronic bioSurveillance (SAGES), collection of modular, freely-available software tools to enable electronic surveillance in these settings. An algorithm test-bed is described and used to compare algorithm alerting performance for both daily and weekly data streams. Multiple detection performance measures are defined, and a practical means of combining them is applied to recommend preferred alerting methods for common scenarios.
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