The Department of Homeland Security Science and Technology Directorate’s “Hidden Signals Challenge” Prize Competition completed Phase I of the challenge on December 4, 2017. In collaboration with the DHS Office of Health Affairs and the National Biosurveillance Integration Center, this competition called upon data innovators from a wide variety of fields to develop concepts for novel uses of existing data that will identify signals and achieve timelier alerts for biothreats in our cities and communities.
Biothreats occur when harmful pathogens are either naturally or deliberately released, posing a risk to national security and public health. Often, biothreats are hard to immediately identify, and their spread can be hard to contain. If and when a potential biothreat appears, every minute counts. Local and national officials must work together to assess the level of risk, develop an action plan, and intervene. Currently, there are a variety of systems and tools in place to identify biothreats, however they rely largely on health data, which presents challenges for real-time alerts and early detection.
Phase I Winners Announced
On February, 14 2018 five finalists were announced with each receiving $20,000 in cash prizes. Finalists from Phase I will have the opportunity to further develop concepts into detailed system designs with guidance from expert mentors. At the end of Stage 2, finalists will be required to submit these detailed system designs, which describe how the concepts from Phase I are to be implemented in practice. The grand prize winner will receive $200,000 in cash prizes. The five finalists are:
- Readiness Acceleration & Innovation Network (RAIN), Tacoma, WA for Commuter Pattern Analysis for Early Biothreat Detection, a system that cross-references de-identified traffic information with existing municipal health data and internet keyword searches. The tool will be developed to recognize commuter absenteeism to flag a possible disease outbreak.
- Vituity, Emeryville, CA for Monitoring emergency department wait times to detect emergent influenza pandemics, a model that alerts authorities of spikes in emergency room wait times that can be attributed to emergent flu pandemics. The solution sources real-time data from a network of 142 hospitals in 19 states and is updated hourly, allowing agencies to quickly intervene.
- William Pilkington & team, Cabarrus County, North Carolina, for One Health Alert System, a symptoms database that analyzes the Daily Disease Report’s top ten symptoms as seen by 43 health care providers in North Carolina. The model flags disease outbreak using textual predictive analytics and accounts for seasonal rates of change.
- Computational Epidemiology Lab at Boston Children's Hospital, Boston, MA for Pandemic Pulse, a tool that integrates six data streams to detect bio-threat signals. First, it alerts agencies using Twitter, Google Search, transportation, news, and HealthMap data of an anomaly in the data stream, then it tracks potential biothreats using live transportation data on Flu Near You.
- Daniel B. Neill and Mallory Nobles, Pittsburgh, PA,for Pre-syndromic Surveillance, a machine learning system that overlays real-time emergency room chief complaint data with social media and news data using the semantic scan, a novel approach to text analysis. The model detects emerging clusters of rare disease cases that do not correspond to known syndrome types.
For more information on this challenge, visit the Hidden Signals Prize Challenge webpage.