Statistical Challenges Facing Early Outbreak Detection in Biosurveillance

• Published in: Technometrics Vol. 52; no. 1; pp. 39 - 51
• Main Authors: Shmueli, Galit, Burkom, Howard
• Format: Journal Article
• Language: English
• Published: Alexandria, VA Taylor & Francis 01-02-2010; The American Society for Quality and The American Statistical Association; American Society for Quality and the American Statistical Association; American Society for Quality
• Subjects: Anomaly Detection; Applied sciences; Biological and medical sciences; Biosurveillance; Bioterrorism; Complaints; Computer science; control theory; systems; Control chart; Data processing. List processing. Character string processing; Disease; Disease models; Disease outbreak; Disease outbreaks; Emergency medical care; Epidemics; Epidemiology; Exact sciences and technology; General aspects; Geographic regions; Health surveillance; Hospitalization; Hospitals; Inference from stochastic processes; time series analysis; Influenza; Inventory control, production control. Distribution; Investigations; Laboratories; Mathematics; Medical sciences; Memory organisation. Data processing; Mortality; Operational research and scientific management; Operational research. Management science; Pharmacy; Probability and statistics; Process controls; Public health; Public health. Hygiene; Public health. Hygiene-occupational medicine; Sciences and techniques of general use; Software; Statistical process control; Statistics; Surveillance; Syndromic data; Telephone hotlines; Time series; Epidemic; Performance evaluation; Statistical process control; Data type; Data analysis; Control chart; Statistical analysis; Time series; Cluster; Epidemiology; Syndromic data; Anomaly detection; Operating conditions; Time dependence; Statistical control; Surveillance; Multiplicity; Disease outbreak; Field inquiry; Monitoring; Public health
• ISSN: 0040-1706; 1537-2723
• DOI: 10.1198/TECH.2010.06134

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.