Modeling and detection of respiratory-related outbreak signatures

We consider time series models for chest radiograph data obtained from Midwest children's emergency departments. These models incorporate available covariate information such as patient visit counts and smoothed ambient temperature series, as well as time series dependencies on daily and weekly seasonal scales. Respiratory-related outbreak signature detection is based on filtering the one-step-ahead prediction errors obtained from the time series models for the respiratory-complaint background.Using simulation experiments based on a stochastic model for an anthrax attack, we illustrate the effect of the choice of filter and the statistical models upon radiograph-attributed outbreak signature detection.We demonstrate the importance of using seasonal autoregressive integrated average time series models (SARIMA) with covariates in the modeling of respiratory-related time series data. We find some homogeneity in the time series models for the respiratory-complaint backgrounds across the Midwest emergency departments studied. Our simulations show that the balance between specificity, sensitivity, and timeliness to detect an outbreak signature differs by the emergency department and the choice of filter. The linear and exponential filters provide a good balance.Well-known, as well as previously uncharacterized infections continue to (re)emerge around the globe. To avoid casualties from outbreaks of these infections and from the potential criminal uses of bioagents, surveillance systems are needed that have the capacity to identify such outbreaks accurately and rapidly. The accuracy and timeliness of biosurveillance systems rests on the ability to model the uncertainty, severity, and aberrancy of clinical symptoms that are likely to portend disease outbreaks as expressed through the data monitoring system. Shmueli [1] summarizes the problems that biosurveillance systems, in general, pose to traditional statistical monitoring: (a) biosurveillance data may not be independent