Statistical Challenges in Modern Biosurveillance

Modern biosurveillance is the monitoring of a wide-range of pre-diagnostic 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 dis- ease surveillance and have also migrated into modern biosurveillance. However, the new types of data monitored, the processes underlying the time series derived from these data, and the applica- tion 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 deflne; data labeling is ambiguous in the sense that outbreak periods are not clearly deflned or known. Additional chal- lenges arise such as multiplicity in several dimensions, performance evaluation, and practical system usage and requirements. Our focus is mainly on the monitoring of time series for early alerting of anomalies to stimulate investigation of potential outbreaks, with a brief summary of methods to detect signiflcant spatial and spatiotemporal case clusters. We discuss the difierent statistical chal- lenges in monitoring modern biosurveillance data, describe the current state of monitoring in the