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A framework for enhancing spatial and temporal granularity in report-based health surveillance systems
Hutchatai Chanlekha1, Ai Kawazoe, Nigel Collier
1National Institute of Informatics, Chiyoda-ku, Tokyo, Japan. hutchatai@nii.ac.jp
BMC Medical Informatics and Decision Making
|January 14, 2010
Summary
A new spatiotemporal zoning scheme improves health surveillance by accurately classifying news articles by event location and time. This reliable method enhances disease outbreak detection through detailed geo-temporal information analysis.
Area of Science:
- Public Health
- Infectious Disease Surveillance
- Geographic Information Systems
Background:
- Public health surveillance systems are crucial for detecting infectious disease outbreaks.
- Existing systems like GPHIN, Argus, HealthMap, and BioCaster rely on geo-temporal encoding of textual data.
- Current methods for geo-temporal information processing may lack granularity for detailed outbreak detection.
Purpose of the Study:
- To introduce a novel spatiotemporal zoning scheme for enhanced health surveillance.
- To improve the processing of geo-temporal information in outbreak detection.
- To enhance the capabilities of Web-based health surveillance systems.
Main Methods:
- Classifying news articles into zones based on spatiotemporal content characteristics.
- Evaluating the reliability of the annotation scheme through inter-annotator agreement analysis.
- Analyzing over 1000 reported events using kappa and percentage agreement metrics.
Main Results:
- The spatiotemporal zoning scheme demonstrated high inter-annotator agreement (kappa > 0.9 for event type, percentage agreement > 0.9 for temporal attributes).
- A slight degradation in agreement was observed for the spatial attribute.
- Annotators achieved the lowest granularity agreement for events indicating outbreak situations.
Conclusions:
- A novel spatiotemporal zoning annotation scheme was developed and evaluated.
- The annotated corpus and scheme are reliable for developing automatic systems.
- Future work includes developing an automatic zoning system for operational health surveillance.
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