Abstract
BackgroundDetecting outbreaks is a crucial task for public health officials, yet gaps remain in the systematic evaluation of outbreak detection protocols. The authors’ objectives were to design, implement, and test a flexible methodology for generating detailed synthetic surveillance data that provides realistic geographical and temporal clustering of cases and use to evaluate outbreak detection protocols.MethodsA detailed representation of the Boston area was constructed, based on data about individuals, locations, and activity patterns. Influenza-like illness (ILI) transmission was simulated, producing 100 years of in silico ILI data. Six different surveillance systems were designed and developed using gathered cases from the simulated disease data. Performance was measured by inserting test outbreaks into the surveillance streams and analyzing the likelihood and timeliness of detection.ResultsDetection of outbreaks varied from 21% to 95%. Increased coverage did not linearly improve detection probability for all surveillance systems. Relaxing the decision threshold for signaling outbreaks greatly increased false-positives, improved outbreak detection slightly, and led to earlier outbreak detection.ConclusionsGeographical distribution can be more important than coverage level. Detailed simulations of infectious disease transmission can be configured to represent nearly any conceivable scenario. They are a powerful tool for evaluating the performance of surveillance systems and methods used for outbreak detection.
Highlights
Detecting outbreaks is a crucial task for public health officials, yet gaps remain in the systematic evaluation of outbreak detection protocols
Influenza-like illness (ILI) surveillance data is characterized by strong seasonal influence and a day of the week pattern, which are evident in the Figure
The surveillance of the simulated ILI counts without any inserted outbreaks informs decisions about the realworld tenability of a surveillance system
Summary
Detecting outbreaks is a crucial task for public health officials, yet gaps remain in the systematic evaluation of outbreak detection protocols. The authors’ objectives were to design, implement, and test a flexible methodology for generating detailed synthetic surveillance data that provides realistic geographical and temporal clustering of cases and use to evaluate outbreak detection protocols. Detecting outbreaks is a crucial task for public health officials. Techniques and methodologies have been developed to screen the increasingly large volumes of data useful to the task [2,3], yet despite this progress, gaps in performance remain. Developing and evaluating outbreak detection is challenging for many reasons. A central difficulty is that the data used to “train” detection algorithms are unique and relatively brief historical samples and do not represent the full range of possible background scenarios.
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