Developments in the Roles, Features, and Evaluation of Alerting Algorithms for Disease Outbreak Monitoring

Automated systems for public health surveillance have evolved over the past several years as national and local institutions have been learning the most effective ways to share and apply population health information for outbreak investigation and tracking. The changes have included developments in algorithmic alerting methodology. This article presents research efforts at The Johns Hopkins University Applied Physics Laboratory for advancing this methodology. The analytic methods presented cover outcome variable selection, background estimation, determination of anomalies for alerting, and practical evaluation of detection performance. The methods and measures are adapted from information theory, signal processing, financial forecasting, and radar engineering for effective use in the biosurveillance data environment. Examples are restricted to univariate algorithms for daily time series of syndromic data, with discussion of future generalization and enhancement.