Booz Allen Hamilton is developing a novel bio-surveillance prototype tool, the Digital Disease Detection Dashboard (D4) to address the questions fundamental to daily biosurveillance analysis and decision making: is something unusual happening (e.g., is an outbreak or novel disease emerging)?, What is the probability that what I’m seeing is by chance?, How confident am I that this data is really detecting a signal?, Why is this happening and can I explain it?; and How many cases should I expect? (e.g., magnitude of event over time). These questions focus on detection, confidence, variance, and forecasting and D4 integrates a number of diverse analytical tools and methods that are crucial to a complete biosurveillance program.
Objective
To develop a web-enabled Digital Disease Detection Dashboard (D4) that allows users to statistically model and forecast multiple data streams for public health biosurveillance. D4 is a user-friendly, cloudenabled, and R Shiny-powered application that provides intuitive visualization enabling immediate situational awareness through interactive data displays and multi-factor analysis of traditional and non-traditional data feeds. The objective of D4 is to support public health decision making with high confidence across all four aspects of the biosurveillance continuum—detection, investigation, response, and prevention.