Title |
A framework for enhancing spatial and temporal granularity in report-based health surveillance systems
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Published in |
BMC Medical Informatics and Decision Making, January 2010
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DOI | 10.1186/1472-6947-10-1 |
Pubmed ID | |
Authors |
Hutchatai Chanlekha, Ai Kawazoe, Nigel Collier |
Abstract |
Current public concern over the spread of infectious diseases has underscored the importance of health surveillance systems for the speedy detection of disease outbreaks. Several international report-based monitoring systems have been developed, including GPHIN, Argus, HealthMap, and BioCaster. A vital feature of these report-based systems is the geo-temporal encoding of outbreak-related textual data. Until now, automated systems have tended to use an ad-hoc strategy for processing geo-temporal information, normally involving the detection of locations that match pre-determined criteria, and the use of document publication dates as a proxy for disease event dates. Although these strategies appear to be effective enough for reporting events at the country and province levels, they may be less effective at discovering geo-temporal information at more detailed levels of granularity. In order to improve the capabilities of current Web-based health surveillance systems, we introduce the design for a novel scheme called spatiotemporal zoning. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
India | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 3% |
Canada | 2 | 3% |
Brazil | 1 | 2% |
Indonesia | 1 | 2% |
United Kingdom | 1 | 2% |
Unknown | 55 | 89% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 13 | 21% |
Student > Ph. D. Student | 11 | 18% |
Researcher | 8 | 13% |
Student > Bachelor | 5 | 8% |
Other | 5 | 8% |
Other | 11 | 18% |
Unknown | 9 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 21 | 34% |
Computer Science | 13 | 21% |
Social Sciences | 5 | 8% |
Nursing and Health Professions | 5 | 8% |
Environmental Science | 2 | 3% |
Other | 8 | 13% |
Unknown | 8 | 13% |