This research will support the BioSense Initiative by developing a software platform for biosurveillance staff? and researchers that allow them to measure the sensitivity, specificity, detection timeliness, and smallest? detectable outbreak of a surveillance system. We refer to these properties as detectability characteristics.? These measurements are critical for evaluating not only the performance of an overall system but also the? performance of outbreak detection algorithms, studying the efficacy of surveillance data sources, and? designing more effective surveillance systems.? The research team has developed a prototype software application called HiFIDE v1.00 that allows users to? assess the sizes and kinds of outbreaks that are detectable using surveillance data such as free-text triage? chief complaints from emergency departments and sales of over-the-counter pharmaceutical. HiFIDE? possesses a graphical user interface (GUI) that enables a user to examine the relationship between? outbreak size, sensitivity, specificity, timeliness of detection, and the completeness of the surveillance data.? The analysis is done using surveillance data for that jurisdiction.? HiFIDE evaluates detectability for a jurisdiction by simulating surveillance data that it would observe during a? real outbreak. HiFIDE forms this outbreak surveillance data by using real non-outbreak surveillance data for? the jurisdiction and injecting the outbreak effect onto this data. HiFIDE v1.00 constructs the inject using data? from a real outbreak in another jurisdiction.? This proposal has the following specific aims:? 1. To enhance HiFIDE by increasing the library of outbreaks and detection algorithms, improving analysis? capability, and expanding features of the GUI.? 2. To develop an Application Programming Interface (API) that enables HiFIDE to use algorithms,? surveillance data, and injects from external surveillance systems to evaluate detectability for those systems.? The communication between HiFIDE and an external system will be PHIN-compliant.? 3. To evaluate the utility of HiFIDE for estimating outbreak features from a spike in surveillance data.
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