Publications

Biologically inspired approaches for biosurveillance anomaly detection and data fusion

Finley, Patrick D.; Finley, Patrick D.; Finley, Patrick D.; Finley, Patrick D.; Levin, Drew L.; Levin, Drew L.; Levin, Drew L.; Levin, Drew L.; Flanagan, Tatiana P.; Flanagan, Tatiana P.; Flanagan, Tatiana P.; Flanagan, Tatiana P.; Beyeler, Walter E.; Beyeler, Walter E.; Beyeler, Walter E.; Beyeler, Walter E.; Mitchell, Michael D.; Mitchell, Michael D.; Mitchell, Michael D.; Mitchell, Michael D.; Ray, Jaideep R.; Ray, Jaideep R.; Ray, Jaideep R.; Ray, Jaideep R.; Moses, Melanie M.; Moses, Melanie M.; Moses, Melanie M.; Moses, Melanie M.; Forrest, Stephanie F.; Forrest, Stephanie F.; Forrest, Stephanie F.; Forrest, Stephanie F.

This study developed and tested biologically inspired computational methods to detect anomalous signals in data streams that could indicate a pending outbreak or bio-weapon attack. Current large- scale biosurveillance systems are plagued by two principal deficiencies: (1) timely detection of disease-indicating signals in noisy data and (2) anomaly detection across multiple channels. Anomaly detectors and data fusion components modeled after human immune system processes were tested against a variety of natural and synthetic surveillance datasets. A pilot scale immune-system-based biosurveillance system performed at least as well as traditional statistical anomaly detection data fusion approaches. Machine learning approaches leveraging Deep Learning recurrent neural networks were developed and applied to challenging unstructured and multimodal health surveillance data. Within the limits imposed of data availability, both immune systems and deep learning methods were found to improve anomaly detection and data fusion performance for particularly challenging data subsets. ACKNOWLEDGEMENTS The authors acknowledge the close collaboration of Scott Lee, Jason Thomas, and Chad Heilig from the US Centers for Disease Control (CDC) in this effort. De-identified biosurveillance data provided by Ken Jeter of the New Mexico Department of Health proved to be an important contribution to our work. Discussions with members of the International Society of Disease Surveillance helped the researchers focus on questions relevant to practicing public health professionals. Funding for this work was provided by Sandia National Laboratories' Laboratory Directed Research and Development program.