TY - JOUR AU - Taylor-McCabe, Kirsten AU - Castro, Lauren AU - Generous, Nicholas AU - Margevicius, Kristen AU - Brown, Mac AU - Deshpande, Alina PY - 2014/03/09 Y2 - 2024/03/29 TI - Contextualizing Data Streams for Infectious Disease Surveillance JF - Online Journal of Public Health Informatics JA - OJPHI VL - 6 IS - 1 SE - Oral Presentations DO - 10.5210/ojphi.v6i1.5064 UR - https://ojphi.org/ojs/index.php/ojphi/article/view/5064 SP - AB - To aid in developing a global biosurveillance program, it is critical to develop a framework to capture and understand the myriad of data streams and evaluate them in context of surveillance goals.  Toward this goal, Los Alamos National Laboratory has developed a new method of evaluating the effectiveness of data stream types through the use of a novel concept called the surveillance window, a technique that integrates operational systems analysis, surveillance system analysis and epidemiological analysis. This study provides a simple, yet elegant methodology for which to ground truth known and emerging data streams for utility in integrated biosurveillance efforts. ER -