Factors affecting automated syndromic surveillance
The increased threat of bioterroristic attacks and epidemic events requires the development of accurate and timely outbreak detection systems for early identification of anomalies in public health data. We propose an automated outbreak detection system based on syndromic data. This system uses an autoregressive model with seasonal components to monitor, online, the daily counts of chief complaints for respiratory syndromes at the emergency department of two major metropolitan hospitals. We evaluate this system by estimating the false positive rate in real data under the assumption that there were no outbreaks of disease, and the true positive rate in real baseline data in which we injected stochastically simulated outbreaks of different shape and size. We then use directed graphical models to account for the effect of exogenous factors on the detection performance of the system. Our study shows that for a week-long outbreak, our model has an overall 84.8% true detection accuracy across all shapes of outbreaks, while the outbreak size influences the earliness to detection. The false and true positive rates are also associated with the exogenous factors and knowledge about these factors can help to improve the detection accuracy. This study suggests that the integration of multiple data sources can significantly improve the detection accuracy of syndromic surveillance systems.
syndromic surveillance graphical models time series model false positive detection true positive detection