Validation of Analytic Methods for Combining Evidence Sources in Biosurveillance

Authors

  • Howard Burkom Johns Hopkins Applied Physics Laboratory, Laurel, MD, United States
  • Yevgeniy Elbert Johns Hopkins Applied Physics Laboratory, Laurel, MD, United States
  • Liane Ramac-Thomas Johns Hopkins Applied Physics Laboratory, Laurel, MD, United States
  • Christopher Cuellar Johns Hopkins Applied Physics Laboratory, Laurel, MD, United States

DOI:

https://doi.org/10.5210/ojphi.v6i1.5201

Abstract

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.

Author Biography

Howard Burkom, Johns Hopkins Applied Physics Laboratory, Laurel, MD, United States

Howard Burkom is a project manager and researcher within the disease surveillance initiative of the Johns Hopkins Applied Physics Laboratory. He was previously a statistical consultant to the Biosense team at CDC, collaborating on system improvements and with health departments on public health applications. An elected member of the ISDS Board Of Directors for 7 years, he has worked exclusively in biosurveillance since 2000, adapting analytic methods from various scientific disciplines for disease monitoring systems.

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Published

2014-03-09

How to Cite

Burkom, H., Elbert, Y., Ramac-Thomas, L., & Cuellar, C. (2014). Validation of Analytic Methods for Combining Evidence Sources in Biosurveillance. Online Journal of Public Health Informatics, 6(1). https://doi.org/10.5210/ojphi.v6i1.5201

Issue

Section

Oral Presentations