A Systematic Evaluation of Traditional and Non-Traditional Data Streams for Integrated Global Biosurveillance (Final Report)
Living in a closely connected and highly mobile world presents many new mechanisms for rapid disease spread and in recent years, global disease surveillance has become a high priority. In addition, much like the contribution of non-traditional medicine to curing diseases, non-traditional data streams are being considered of value in disease surveillance. Los Alamos National Laboratory (LANL) was tasked by the Defense Threat Reduction Agency (DTRA)-Joint Science and Technology Office (JSTO) to determine the relevance of data streams for an integrated global biosurveillance system through the use of defined metrics and methodologies. The long-term, broad objective of this project was to provide information and analysis that can be leveraged from existing and developing national and international disease surveillance systems and methodologies, to create a global disease monitoring system, ultimately providing decision makers with timely information to prepare for and mitigate the spread of disease. Specifically, this project evaluated data streams that are currently being used in surveillance systems and data streams that had the potential for being used in surveillance to enable early disease detection. LANL’s focus was on infectious disease surveillance and the term “biosurveillance” will be used to refer to this scope hereafter. Data stream evaluation was conducted using two different methods: Multi criteria decision analysis (MCDA) using a tool called Logical Decisions ® that assigns utility scores to data streams based on weighted metrics and assigned values specific to data stream categories; and a Surveillance Window concept developed at LANL that assigns a window or windows of time specific to a disease within which information coming from various data streams can be determined to have utility. A cross method analysis was performed between the surveillance window based evaluation and the MCDA-based evaluation to identify data stream categories that showed high utility for both methods. In addition, algorithms that can integrate useful data streams to facilitate early disease detection were also examined. This project provided an understanding of data streams relevant to early warning, early detection and monitoring of disease outbreaks. The report describes the results of LANL’s evaluation of 16 data stream categories using the two methods, and recommendations for next steps. The project evolved significantly over its course, and significant improvements in our approaches were made compared to the initial proposed processes. A key outcome of this effort was that LANL was able to bring together several diverse entities involved in disease surveillance and lay the foundation for new collaborations that straddled military and civilian health surveillance. The robust evaluation framework developed by LANL has generated significant interest and there is interest in adopting this framework for various applications both within disease surveillance as well as other public health initiatives. Finally, LANL developed the Biosurveillance Resource Directory (BRD), a relational database that underwent pilot testing by members of the human, plant and animal disease surveillance community. The BRD is intended to be a global resource to facilitate rapid information access.