Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Activity monitoring: noticing interesting changes in behavior
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Rule-based anomaly pattern detection for detecting disease outbreaks
Eighteenth national conference on Artificial intelligence
Power comparisons for disease clustering tests
Computational Statistics & Data Analysis
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
On detecting space-time clusters
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Qualitative spatial reasoning with topological information
Qualitative spatial reasoning with topological information
The Journal of Machine Learning Research
Epidemic Alert & Response Framework and Technology Based on Spreading Dynamics Simulation
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
A Temporal Extension of the Bayesian Aerosol Release Detector
BioSecure '08 Proceedings of the 2008 International Workshop on Biosurveillance and Biosecurity
A Z-Score Based Multi-level Spatial Clustering Algorithm for the Detection of Disease Outbreaks
BioSecure '08 Proceedings of the 2008 International Workshop on Biosurveillance and Biosecurity
Biosurveillance of emerging biothreats using scalable genotype clustering
Journal of Biomedical Informatics
Surveillance to detect emerging space-time clusters
Computational Statistics & Data Analysis
Comparing early outbreak detection algorithms based on their optimized parameter values
Journal of Biomedical Informatics
Algorithm combination for improved performance in biosurveillance systems
BioSurveillance'07 Proceedings of the 2nd NSF conference on Intelligence and security informatics: BioSurveillance
Multiple attribute frequent mining-based for dengue outbreak
ADMA'10 Proceedings of the 6th international conference on Advanced data mining and applications: Part I
A review of public health syndromic surveillance systems
ISI'06 Proceedings of the 4th IEEE international conference on Intelligence and Security Informatics
The picture of health: map-based, collaborative spatio-temporal disease tracking
Proceedings of the First ACM SIGSPATIAL International Workshop on Use of GIS in Public Health
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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. Published by Elsevier Inc.