The Johns Hopkins Applied Physics Laboratory and the Armed Forces Health Surveillance Center have developed a hybrid processing engine that alerts monitors when a severe health condition exists based on corroboration among several sources of data. The system was designed to ingest a day's worth of recent data and provide results to monitors daily. In some theaters, the health of the US Forces must be determined at near-real time rates requiring a reassessment of current surveillance practices. Challenges exist in both acquiring data in real-time and in modifying automated alerting processes to re-evaluate as a new piece of evidence is received.
Objective
To develop a real-time surveillance capability that processes, fuses and assesses when data is received using a new fusion processing methodology and multiple sources health indicator data.