Thursday, December 12, 2013; 9:50 – 10:10am
Grand Ballroom D
Howard Burkom, Johns Hopkins Applied Physics Laboratory
To manage an increasingly complex data environment, a fusion module based on Bayesian networks (BN) was developed for the Dept. of Defense (DoD) Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE). Subsequent efforts have produced a full fusion-enabled version of ESSENCE for beta testing and further upgrades. The current presentation describes advances to formalize the network training, calibrate the component alerting algorithms and decision nodes together, and implement a validation strategy. A cross-validation strategy produced consistent threshold combinations yielding 88% sensitivity from reported events, a 10-15% improvement over the original demonstration module.
Online Journal of Public Health Informatics. 2014; 6(1).