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.
%0 Journal Article
%1 buckeridge05algorithms
%A Buckeridge, David L.
%A Burkom, Howard
%A Campbell, Murray
%A Hogane, William R.
%A Moore, Andrew W.
%D 2005
%J J. Biomedical Informatics
%K AnomalyDetection
%P 99--113
%T Algorithms for rapid outbreak detection: a research synthesis
%U http://www.samsi.info/200506/ndhs/workinggroup/ad/Papers/Buckeridge_etal_2005.pdf
%V 38
%X 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.
@article{buckeridge05algorithms,
abstract = {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.},
added-at = {2006-09-19T17:26:09.000+0200},
author = {Buckeridge, David L. and Burkom, Howard and Campbell, Murray and Hogane, William R. and Moore, Andrew W.},
biburl = {https://www.bibsonomy.org/bibtex/2ecef74f3dcbf96780d5e75af4d0b7f64/kaixo},
description = {Anomaly Detection},
interhash = {0aa9244df22d5cac0f431ffee1e3d9ec},
intrahash = {ecef74f3dcbf96780d5e75af4d0b7f64},
journal = {J. Biomedical Informatics},
keywords = {AnomalyDetection},
pages = {99--113},
timestamp = {2006-09-19T17:26:09.000+0200},
title = {Algorithms for rapid outbreak detection: a research synthesis},
url = {http://www.samsi.info/200506/ndhs/workinggroup/ad/Papers/Buckeridge_etal_2005.pdf},
volume = 38,
year = 2005
}