The Biosurveillance Ecosystem (BSVE) is a rapidly emerging capability being developed by the DTRA Chemical and Biological Technologies Department to enable real-time biosurveillance for early warning and course of action analysis. Retrieval and integration of multiple sources of information are required in order to provide high confidence models for prediction, early warning and forecasting of disease events. Reliable and automated on-demand analytics are key components in such biosurveillance efforts and offer mitigation strategies. While a number of diagnostic devices can immediately contribute to biosurveillance, the use of next generation sequencing (NGS) as a means of surveillance is becoming routine, driven in part by large investments by the DoD and the CDC in placing current day sequencing instruments at a large number of venues worldwide, and by the NIH with routine human clinical studies. We have developed a number of algorithms and tools that can process genomic information to identify signatures of pathogenic agents, and to characterize their functional potential. We have begun implementing these applications together with sequencing capability in international settings, such as Eurasia (Georgia), Africa (Uganda, Gabon and Kenya), Asia (Thailand, Cambodia), the Middle East (Jordan), and South America (Peru).
We are currently developing an analytic application in the BSVE that: 1) can retrieve raw and processed genomic information generated and streamed from international sites, and 2) display the pathogens identified in the datasets in a geo-temporal manner along with other sample metadata. This work leverages our previous efforts in developing flexible, extensible, and rapid bioinformatics capabilities, directly supporting engagement of domestic and international partners during a disease outbreak event. Because sequencing data will be generated at a number of central processing laboratories (e.g. CDC or DoD laboratories, both CONUS and OCONUS), we have implemented metadata standards to augment the raw data with predictive power, to track geographic origins of samples, and to potentially relate these data with patient symptoms and disease spread. The integration of these disparate data types will lead to significantly improved prediction and early warning of disease outbreaks along with potential for suggested mitigation strategies, and together with disease-specific models, can provide refined forecasting of spread and impact.
Gene editing, synthetic genomics, forensics, and biosurveillance
