Statistical Challenges Facing Early Outbreak Detection in Biosurveillance

Abstract

Abstract Modern biosurveillance is the monitoring of a wide range of prediagnostic and diagnostic data for the purpose of enhancing the ability of the public health infrastructure to detect, investigate, and respond to disease outbreaks. Statistical control charts have been a central tool in classic disease surveillance and also have migrated into modern biosurveillance; however, the new types of data monitored, the processes underlying the time series derived from these data, and the application context all deviate from the industrial setting for which these tools were originally designed. Assumptions of normality, independence, and stationarity are typically violated in syndromic time series. Target values of process parameters are time-dependent and hard to define, and data labeling is ambiguous in the sense that outbreak periods are not clearly defined or known. Additional challenges include multiplicity in several dimensions, performance evaluation, and practical system usage and requirements. Our focus is mainly on the monitoring of time series to provide early alerts of anomalies to stimulate investigation of potential outbreaks, with a brief summary of methods to detect significant spatial and spatiotemporal case clusters. We discuss the statistical challenges in monitoring modern biosurveillance data, describe the current state of monitoring in the field, and survey the most recent biosurveillance literature. Keywords: : Anomaly detectionControl chartDisease outbreakStatistical process controlSyndromic data