دورية أكاديمية
Algorithms for rapid outbreak detection: a research synthesis.
| العنوان: | Algorithms for rapid outbreak detection: a research synthesis. |
|---|---|
| المؤلفون: | Buckeridge DL; Palo Alto VA Health Care System, Palo Alto, CA, USA. david.buckeridge@stanford.edu, Burkom H, Campbell M, Hogan WR, Moore AW |
| المصدر: | Journal of biomedical informatics [J Biomed Inform] 2005 Apr; Vol. 38 (2), pp. 99-113. |
| نوع المنشور: | Comparative Study; Evaluation Study; Journal Article; Research Support, U.S. Gov't, Non-P.H.S. |
| اللغة: | English |
| بيانات الدورية: | Publisher: Elsevier Country of Publication: United States NLM ID: 100970413 Publication Model: Print Cited Medium: Print ISSN: 1532-0464 (Print) Linking ISSN: 15320464 NLM ISO Abbreviation: J Biomed Inform Subsets: MEDLINE |
| أسماء مطبوعة: | Publication: Orlando : Elsevier Original Publication: San Diego, CA : Academic Press, c2001- |
| مواضيع طبية MeSH: | Algorithms* , Databases, Factual* , Decision Support Techniques*, Communicable Diseases/*diagnosis , Diagnosis, Computer-Assisted/*methods , Disease Outbreaks/*prevention & control , Population Surveillance/*methods, Bioterrorism/prevention & control ; Disease Notification/methods ; Humans ; Information Storage and Retrieval/methods ; Reproducibility of Results ; Research ; Sensitivity and Specificity |
| مستخلص: | The threat of bioterrorism has stimulated interest in enhancing public health surveillance to detect disease outbreaks more rapidly than is currently possible. To advance research on improving the timeliness of outbreak detection, the Defense Advanced Research Project Agency sponsored the Bio-event Advanced Leading Indicator Recognition Technology (BioALIRT) project beginning in 2001. The purpose of this paper is to provide a synthesis of research on outbreak detection algorithms conducted by academic and industrial partners in the BioALIRT project. We first suggest a practical classification for outbreak detection algorithms that considers the types of information encountered in surveillance analysis. We then present a synthesis of our research according to this classification. The research conducted for this project has examined how to use spatial and other covariate information from disparate sources to improve the timeliness of outbreak detection. Our results suggest that use of spatial and other covariate information can improve outbreak detection performance. We also identified, however, methodological challenges that limited our ability to determine the benefit of using outbreak detection algorithms that operate on large volumes of data. Future research must address challenges such as forecasting expected values in high-dimensional data and generating spatial and multivariate test data sets. |
| تواريخ الأحداث: | Date Created: 20050331 Date Completed: 20050726 Latest Revision: 20191210 |
| رمز التحديث: | 20231215 |
| DOI: | 10.1016/j.jbi.2004.11.007 |
| PMID: | 15797000 |
| قاعدة البيانات: | MEDLINE |
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