TY - GEN
T1 - Understanding syndromic hotspots - A visual analytics approach
AU - Maciejewski, Ross
AU - Rudolph, Stephen
AU - Hafen, Ryan
AU - Abusalah, Ahmad
AU - Yakout, Mohamed
AU - Ouzzani, Mourad
AU - Cleveland, William S.
AU - Grannis, Shaun J.
AU - Wade, Michael
AU - Ebert, David S.
PY - 2008
Y1 - 2008
N2 - When analyzing syndromic surveillance data, health care officials look for areas with unusually high cases of syndromes. Unfortunately, many outbreaks are difficult to detect because their signal is obscured by the statistical noise. Consequently, many detection algorithms have a high false positive rate. While many false alerts can be easily filtered by trained epidemiologists, others require health officials to drill down into the data, analyzing specific segments of the population and historical trends over time and space. Furthermore, the ability to accurately recognize meaningful patterns in the data becomes more challenging as these data sources increase in volume and complexity. To facilitate more accurate and efficient event detection, we have created a visual analytics tool that provides analysts with linked geo-spatiotemporal and statistical analytic views. We model syndromic hotspots by applying a kernel density estimation on the population sample. When an analyst selects a syndromic hotspot, temporal statistical graphs of the hotspot are created. Similarly, regions in the statistical plots may be selected to generate geospatial features specific to the current time period. Demographic filtering can then be combined to determine if certain populations are more affected than others. These tools allow analysts to perform real-time hypothesis testing and evaluation.
AB - When analyzing syndromic surveillance data, health care officials look for areas with unusually high cases of syndromes. Unfortunately, many outbreaks are difficult to detect because their signal is obscured by the statistical noise. Consequently, many detection algorithms have a high false positive rate. While many false alerts can be easily filtered by trained epidemiologists, others require health officials to drill down into the data, analyzing specific segments of the population and historical trends over time and space. Furthermore, the ability to accurately recognize meaningful patterns in the data becomes more challenging as these data sources increase in volume and complexity. To facilitate more accurate and efficient event detection, we have created a visual analytics tool that provides analysts with linked geo-spatiotemporal and statistical analytic views. We model syndromic hotspots by applying a kernel density estimation on the population sample. When an analyst selects a syndromic hotspot, temporal statistical graphs of the hotspot are created. Similarly, regions in the statistical plots may be selected to generate geospatial features specific to the current time period. Demographic filtering can then be combined to determine if certain populations are more affected than others. These tools allow analysts to perform real-time hypothesis testing and evaluation.
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U2 - 10.1109/VAST.2008.4677354
DO - 10.1109/VAST.2008.4677354
M3 - Conference contribution
AN - SCOPUS:77949825638
SN - 9781424429356
T3 - VAST'08 - IEEE Symposium on Visual Analytics Science and Technology, Proceedings
SP - 35
EP - 42
BT - VAST'08 - IEEE Symposium on Visual Analytics Science and Technology, Proceedings
T2 - IEEE Symposium on Visual Analytics Science and Technology, VAST'08
Y2 - 21 October 2008 through 23 October 2008
ER -