Volume 2 Supplement 3
Visualization techniques and graphical user interfaces in syndromic surveillance systems. Summary from the Disease Surveillance Workshop, Sept. 11–12, 2007; Bangkok, Thailand
© Moore et al; licensee BioMed Central Ltd. 2008
Published: 14 November 2008
Timeliness is a critical asset to the detection of public health threats when using syndromic surveillance systems. In order for epidemiologists to effectively distinguish which events are indicative of a true outbreak, the ability to utilize specific data streams from generalized data summaries is necessary. Taking advantage of graphical user interfaces and visualization capacities of current surveillance systems makes it easier for users to investigate detected anomalies by generating custom graphs, maps, plots, and temporal-spatial analysis of specific syndromes or data sources.
Although electronic surveillance systems are able to automatically detect statistical anomalies in syndromic data, this creates an alert that needs epidemiological investigation. Human interpretation is required to interpret the alerts and raw data to separate statistically significant but epidemiologically unimportant events from real disease outbreaks. To this end, effective syndromic surveillance systems incorporate a graphical user interface and multiple data visualization techniques in order to aid epidemiologists in deciphering large amounts of data in a timely and cost effective manner. The presence of data tables or line listings allows easy insight into recent system activity. These tables will visually highlight any instances where the data has exceeded the statistically predicted range of values and allow the epidemiologist to confirm any statistical aberration alerts. This allows users to quickly identify potential outbreak situations and also provides them with extra information to further investigate the situation such as the geographic area, population, statistically predicted values, received values, and upper confidence limits. Automatically generated time series plots allow epidemiologists to examine recent trends (monthly, yearly, seasonal) from incoming data streams.
Geospatial visualization of data helps users to identify the significance of any recent data anomalies. By viewing a map generated by an integrated geographic information system, epidemiologists can identify clusters of increased activity or determine if the increases in data are randomly dispersed. Such geospatial visualization can also assist public health in tracking an outbreak, by creating maps of received data overlaid with infrastructure, water sources, or hospital locations.
By making it faster and simpler for epidemiologists to analyze large amounts of data, a well-designed user interface ensures cost effective surveillance and timely outbreak detection.
One of the greatest assets of syndromic surveillance systems is their timeliness. These systems receive data in real-time or near real-time, and thus have the potential to detect changes in the public's health more rapidly than traditional reporting methods. Although these systems can automatically gather, parse, and analyze syndromic data sources, for the most part the alerts generated are merely suggestive, and not conclusive . Human interpretation is needed to examine the data gathered by surveillance systems and to determine when a public health response is warranted. Since these systems can collect data automatically and in real-time, the timeliness of outbreak detection is largely dependant on how long it takes epidemiologists to survey the gathered data and separate real disease outbreaks from false alarms. Furthermore, real disease outbreaks are scarce, and so for syndromic surveillance systems to be cost effective, it is important to reduce as much as possible the time needed for day-to-day monitoring by epidemiologists. To this end, effective surveillance systems incorporate graphical user interfaces and multiple data visualization techniques to make it easy for epidemiologists to examine large quantities of data and routing alerts in a timely and cost effective manner.
Certain recently developed surveillance systems have combined temporal and spatial analysis to further facilitate the investigation of data trends and anomalies. The ability to simultaneously view temporal changes and spatial distributions of syndromic data can be helpful in visualizing the propagation of statistically significant events. The SendSS syndrome surveillance module – constructed by the Georgia Division of Public Health – integrates a spatio-temporal visualization interface, which plots semi-transparent circles on a regional map with the circle radius representative of the magnitude of the data . The data presented on the map can be moved forward or backward in time one day at a time, or can be animated to show all daily changes over an interval. The BioPortal Project prototype system includes a Spatial Temporal Visualizer for data visualization . This tool simultaneously presents a periodic spiral graph of user selected granularity (year, month, week, and day), a two-dimensional timeline of data received, and a GIS view of the spatial distribution of the data contained in the selected interval . Presenting all of this data in the same window can make it easier for users to examine seasonal or weekly trends in the data, view temporal trends, and spatially represent a selected interval of data.
The graphical user interfaces (GUI) and visualization techniques can be evaluated by the Framework recommended from the CDC working group for Evaluating Public health Surveillance Systems . The user interface should be acceptable to the epidemiologists and this will be directly related to its simplicity of use, functionality and ongoing educational programs for the end user. Roll based access to a hierarchy of epidemiological tools may enhance acceptance. Automated report generation, data export and drill down capability will also enhance functionality.
Flexibility of the GUI will allow for a static interface for some users, while advanced users can access increased functionality and a more dynamic interface. Advanced users may want adaptable modifiable syndrome classifications, multiple anomaly capabilities for each data set, or integration of multiple data streams. The dependability of the system is integral, and hence the GUI must be able to inform and allow investigation by the end user regarding data flow interruption, quality and missing data. Sustainability of systems is enhanced if the GUI is part of the normal data access and reporting systems of public health authorities, allowing for integration with reportable disease and laboratory based systems. Hence making the syndromic surveillance GUI a module of an integrated public health information system could facilitate acceptance. To ensure representativeness of the system the GUI should be able to allow visualization of complementary data sets such as telehealth, laboratory, emergency department and pharmacy data.
Recent advances in the complexity of syndromic surveillance systems allow epidemiologists and other users to receive data in real-time, and provide useful visual interfaces to assist in interpretation. Both spatial and temporal data are often available, and the ability to simultaneously view temporal changes and spatial distributions of syndromic data can be helpful to examine seasonal or weekly trends, view temporal trends, and spatially represent a selected interval of data.
GE did the literature search and initial draft. AK reviewed the figures and refined the literature search. KM reviewed, edited and modified all drafts and is responsible for final content. KM conceived of the topic and analysis. All authors read and approved the final manuscript.
List of abbreviations used
Arc Internet Map Server
US Centers for Disease Control and Prevention
Electronic Surveillance System for the Early Notification of Community-based Epidemics
Geographic Information System
National Center of Excellence for Infectious Disease Informatics
Real-time Outbreak and Disease Surveillance
State Electronic Notifiable Disease Surveillance System.
Special thanks to U.S. Department of Defense Global Emerging Infections Surveillance and Response System; The John Hopkins University Applied Physics Laboratory; and Armed Forces Research Institute of Medical Sciences.
This article has been published as part of BMC Proceedings Volume 2 Supplement 3, 2008: Proceedings of the 2007 Disease Surveillance Workshop. Disease Surveillance: Role of Public Health Informatics. The full contents of the supplement are available online at http://www.biomedcentral.com/1753-6561/2?issue=S3.
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