Bayesian Modeling for Biosurveillance
Publications
[Cami 2008a] Cami A, Wallstrom
GL, Hogan WR.
Effect of commuting on the
detection
and characterization performance of the Bayesian aerosol release
detector. In:
Proceedings of the Bioinformatics and Biomedicine Workshops,
IEEE
International Conference (2008) 91 -98. [
pdf]
[Cami
2008b] Cami A, Wallstrom
GL, Hogan WR. Integrating a commuting model with the Bayesian aerosol
release detector.
In: Proceeding of BioSecure (2008) 85-96. [pdf]
[Chen
2008] Chen L, Dubrawski A, Ray S, Baysek M, Kelley L, Dunham A,
Huckabee M, Fedorka-Cray
PJ, Jackson C, McGlinchey B. Detecting linkages between human illness
and
Salmonella isolates in food using a new tool for spatio-temporal
analysis of
multistream data. In: Proceedings of the Symposium of the American
Medical
Informatics Association (2008) 900. [pdf]
[Cooper 2004]
Cooper GF, Dash DH, Levander JD, Wong WK, Hogan WR, and Wagner MM.
Bayesian biosurveillance of disease outbreaks. Proceedings of the
Conference on Uncertainty in Artificial Intelligence (2004) 94-103. [pdf]
[Cooper 2006] Cooper
GF, Dowling
JN, Levander JD, Sutovsky P. A Bayesian
algorithm for detecting CDC Category A outbreak diseases from emergency
dept
chief complaints. In: Advances
in Disease Surveillance 2 (2006) 45. [pdf]
[Das 2007] Das K, Schneider J.
Detecting anomalous records in categorical datasets. In: Proceeding of
the ACM
SIGKDD Conference on Knowledge Discovery and Data Mining (2007)
220-229. [pdf]
[Das
2008a] Das K, Schneider J,
Neill D. Anomaly pattern detection in categorical datasets. In:
Proceeding of
the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2008)
169-176. [pdf]
[Das
2008b] Das K, Schneider J,
Neill DB. Anomaly pattern detection for biosurveillance. Advances in
Disease
Surveillance 5 (2008) 19. [pdf]
[Das 2009]
Das K. Anomalous
Pattern Detection. Doctoral Dissertation, Machine Learning Department, Carnegie Mellon University
(2009). [pdf]
[Dubrawski
2007a] Dubrawski A, Baysek M, Mikus S,
McDaniel C, Mowry B, Moyer L, Ostlund J, Sondheimer N, Stewart T.
Applying
outbreak detection algorithms to prognostics. In: Proceedings of the
AAAI Fall
Symposium on Artificial Intelligence in Prognostics (2007). [pdf]
[Dubrawski
2007b] Dubrawski A, Ostlund J, Chen L, Moore AW.
Computationally
efficient scoring of activity in large social networks using
connectivity
patterns and demographics of entities. In: Proceedings of the Workshop
on
Artificial Intelligence and Data Mining (2007). [pdf]
[Dubrawski 2007c] Dubrawski A, Sabhnani M, Ray S, Roure J,
XBaysek M, T-Cube as an enabling technology in surveillance
applications,
Advances in Disease Surveillance 4 (2007)
6. [
pdf]
[Dubrawski
2008a] Dubrawski A, Sabhnani M, Ray S, Baysek M, Chen L, Ostlund J,
Knight M. Interactive
manipulation, visualization and analysis of large sets of
multidimensional time
series in health informatics. In: Proceedings of the INFORMS Workshop
on Data
Mining and Health Informatics (2008). [pdf]
[Dubrawski
2008b] Dubrawski A, Chen L, Ostlund J. Using the AFDL algorithm to
estimate the
risk of positive outcomes of microbial tests at food establishments.
Advances
in Disease Surveillance 5 (2008) 102. [pdf]
[Dubrawski
2009a] Dubrawski A. Detection of events in multiple streams of
surveillance data.
In: Castillo-Chavez C, Chen H, Lober W, Thurmond M, and Zeng D (eds.)
Infectious
Disease Informatics: Public Health and Biodefense (Springer-Verlag,
2009 in
press). [pdf]
[Dubrawski
2009b] Dubrawski A, Sabhnani M, Knight M, Baysek M, Neill D, Ray S,
Michalska
A, Waidyanatha N. T-Cube web interface in support of a real-time
bio-surveillance program, (extended demo abstract). In: Proceedings of
the
IEEE/ACM International Conference on Information and Communication
Technologies
and Development (2009). [pdf]
[Dubrawski
2009c] Dubrawski A, Sarkar P, Chen L. Trade-offs between agility and
reliability of predictions in dynamic social networks used to model
risk of
microbial contamination of food. In: Proceedings of the International
Conference on Advances in Social Networks Analysis and Mining, Athens, Greece
(2009). [pdf]
[Espino 2007] Espino
J, Dowling J, Levander J, Sutovsky P, Wagner MW, Cooper GF, SyCo: A
Probabilistic Machine Learning Method for Classifying Chief Complaints
into Symptom and Syndrome Categories, Advances in Disease
Surveillance 2 (2007) 5. [pdf]
[Hogan 2006] Hogan
WR. Chapter
19: Atmospheric dispersion modeling in biosurveillance. In: Wagner MM, Moore AW, Aryel
RM (eds.) Handbook
of Biosurveillance (Academic Press, 2006). [pdf]
[Hogan 2007] Hogan
WR, Cooper GF,
Wallstrom GL, Wagner MM, Depinay JM. The Bayesian aerosol release
detector: An
algorithm for detecting and characterizing outbreaks caused by an
atmospheric
release of Bacillus Anthracis.
Statistics in Medicine 26 (2007) 5225-5252. [pdf]
[Jaing 2006] Jiang
X, Wallstrom GL. A Bayesian Network for Outbreak Detection and
Prediction. Proceedings of AAAI-06 (2006) 1155-1160. [pdf]
[Jaing 2007a] Jiang
X, Cooper GF, Levander J. A Bayesian network model for spatial cluster
detection. UAI Workshop on Bayesian Modeling Applications (2007). [pdf]
[Jiang 2007b] Jiang,
X, and Cooper, GF. A recursive algorithm for spatial cluster detection.
In:
Proceedings of the Symposium of the American Medical Informatics
Association
(AMIA) (2007): 369-373. [pdf]
[Jiang
2008a] Jiang X. A Bayesian
Network Model for Spatiotemporal Event Detection. Doctoral
dissertation,
Department of Biomedical Informatics, University of Pittsburgh
(2008).
[Jiang 2008b] Jiang X,
Cooper GF. A temporal method for
outbreak detection using a
Bayesian network. Advances in Disease Surveillance 5 (2008) 105. [pdf]
[Jiang 2008c] Jiang
X, Wagner MM, Cooper GF, Modeling the temporal trend of the daily
severity of an outbreak using Bayesian networks, Innovations in
Bayesian Networks of Studies in Computational Intelligence,
Springer-Verlag, (2008). [link]
[Jiang 2009a] Jiang
XI, Neill DB,
Cooper GF. On the robustness of Bayesian network based spatial event
surveillance. International Journal of Approximate Reasoning (to
appear).
[Jiang 2009b] Jiang X,
Cooper GF.
A real-time temporal Bayesian architecture for event
surveillance
and its application to patient-specific multiple disease outbreak
detection.
Data Mining and Knowledge Discovery (to appear). [pdf]
[Jiang
2009c] Jiang X, Cooper GF.
A Bayesian spatio-temporal method for disease outbreak detection (under
review).
[Jiang
2009d] Jiang X, Cooper GF, Neill
DB. Generalized AMOC curves for evaluation and improvement of event
surveillance. In: Proceedings of the Symposium of the American Medical
Informatics Association (2009). [pdf]
[Kong 2008] Kong X, Wallstrom GL, Hogan WR. A
temporal extension of
the Bayesian aerosol release detector. In: Proceedings of BioSecure
(2008)
97–107. [pdf]
[Kulldorf
2007] Kulldorff M,
Mostashari F, Luiz FD, Yih K, Kleinman K, Platt R. Multivariate scan
statistics
for disease surveillance. Statistics in Medicine 26
(2007). [pdf]
[Makatchev
2008a] Makatchev M,
Neill D. Learning outbreak regions in Bayesian spatial scan statistics.
In:
Proceedings of the ICML/UAI/COLT Workshop on Machine Learning for
Health Care
Applications (2008). [pdf]
[Makatchev
2008b] Makatchev M,
Neill DB. Learning outbreak regions for Bayesian spatial
biosurveillance.
Advances in Disease Surveillance 5 (2008) 45. [pdf]
[Neill
2004a] Neill DB, Moore AW.
A fast multi-resolution method for detection of significant spatial
disease
clusters. Advances in Neural Information Processing Systems 16 (2004)
651-658. [pdf]
[Neill
2004b] Neill DB, Moore AW.
Rapid detection of significant spatial clusters. In: Proceedings of the
ACM
SIGKDD Conference on Knowledge Discovery and Data Mining (2004)
256-265. [pdf]
[Neill 2005a] Neill
DB, Moore AW.
Efficient scan statistic computations. In A. Lawson and K. Kleinman
(eds.)
Spatial and Syndromic Surveillance for Public Health (2005) 189-202.
[Neill
2005b] Neill DB, Moore AW,
Pereira F, Mitchell T. Detecting significant multidimensional spatial
clusters.
Advances in Neural Information Processing Systems 17 (2005) 969-976. [pdf]
[Neill
2005c] Neill DB, Moore AW, Sabhnani MR, Daniel K. Detection of
emerging
space-time clusters. In: Proceedings of the ACM SIGKDD Conference on
Knowledge
Discovery and Data Mining (2005) 218-227. [pdf]
[Neill
2006a] Neill DB. Detection of Spatial and Spatio-Temporal
Clusters. Doctoral
Disseration, School of Computer Science, Carnegie Mellon
University (2006).
[pdf]
[Neill
2006b] Neill DB, Moore AW, Cooper GF. A Bayesian spatial scan
statistic.
Neural Information Processing Systems
18 (2006) 1003-1010. [pdf]
[Neill 2006c] Neill
DB, Moore AW, and Cooper GF. A Bayesian scan statistic for spatial
cluster detection. Advances in Disease Surveillance 1 (2006) 55. [pdf]
[Neill
2007a] Neill DB, Sabhnani MR. A robust expectation-based
spatial scan
statistic. Advances in Disease
Surveillance 2 (2007) 61. [pdf]
[Neill
2007b] Neill DB, Lingwall
J. A nonparametric scan statistic for multivariate disease
surveillance. Advances
in Disease Surveillance 4 (2007) 106. [pdf]
[Neill
2007c] Neill DB, Moore AW,
Cooper GF. A multivariate Bayesian scan statistic. Advances in Disease
Surveillance 2 (2007) 60. [pdf]
[Neill
2007d] Neill DB.
Incorporating learning into disease surveillance systems. Advances in
Disease
Surveillance 4 (2007) 107. [pdf]
[Neill
2008] Neill DB. Fast and
flexible outbreak detection by linear-time subset scanning. Advances in
Disease
Surveillance 5 (2008) 48. [pdf]
[Neill
2009a] Neill DB.
Expectation-based scan statistics for monitoring spatial time series
data.
International Journal of Forecasting 25 (2009) 498-517. [pdf]
[Neill
2009b] Neill DB. An
empirical comparison of spatial scan statistics for outbreak detection.
International Journal of Health Geographics 8 (2009) 20. [pdf]
[Neill
2009c] Neill DB, Cooper
GF. A multivariate Bayesian scan statistic for early event detection
and
characterization. Machine Learning (in press). [pdf]
[Neill
2009d] Neill DB, Cooper GF,
Das K, Jiang X, Schneider J. Bayesian network scan statistics for
multivariate
pattern detection. In: J. Glaz, V. Pozdnyakov, and S. Wallenstein
(eds.) Scan
Statistics: Methods and Applications (Birkhäuser, 2009). [pdf]
[Que
2008a] Que J, Tsui FC. A
multilevel spatial clustering algorithm for detection of disease
outbreaks. In:
Proceedings to the Symposium of the American Medical Informatics
Association (2008)
611-615. [pdf]
[Que
2008b] Que J, Tsui FC, Espino
J. A Z-Score based multi-level spatial clustering algorithm for the
detection
of disease outbreaks. In: Proceedings of Biosecure (2008) 108-118. [pdf]
[Que 2009]
Que J, Tsui FC. Rank-based
spatial clustering: A framework for rapid outbreak detection (under
review).
[Ray
2008] Ray S, Michalska A, Sabhnani M, Dubrawski A, Baysek M, Chen L,
Ostlund J.
Tcube web interface: A tool for immediate visualization, interactive
manipulation and analysis of large sets of multivariate time series.
In:
Proceedings of the Symposium of the American Medical Informatics
Association (2008)
1106.
[Rolka 2007] Rolka H, Burkom
H, Cooper GF, Kulldorff M, Madigan D, Wong WK. Issues in applied
statistics for public health bioterrorism surveillance using multiple
data streams: Research needs. Statistics In Medicine 26 (2007)
1834-1856. [pdf]
[Roure
2007a ] Roure J, Dubrawski A, Schneider J. A study into detection of
bio-events
in multiple streams of surveillance data. In D. Zeng et al. (eds.):
BioSurveillance 2007, Lecture Notes in Computer Science 4506 (2007)
124–133. [pdf]
[Roure 2007b] Roure
J, Dubrawski A, Schneider J. Learning specific detectors of adverse
events in multivariate time series. Advances in Disease Surveillance 4
(2007) 111. [pdf]
[Roure
2008] Roure J, Dubrawski A, Schneider J. Learning detectors of events
in
multivariate time series. In: Proceedings of the Symposium of the
American
Medical Informatics Association (2008) 171. [pdf]
[Sabhanani 2005a]
Sabhnani MR, Neill DB, et al. Detecting anomalous patterns in pharmacy
retail data. Proceedings of the KDD Workshop on Data Mining Methods for
Anomaly Detection 7 (2005) 132-137. [pdf]
[Sabhanani 2005b] Sabhnani MR, Neill DB, Moore AW, Dubrawski AW, Wong
WK. Efficient analytics for effective monitoring of biomedical
security. Proceedings of the IEEE International Conference on
Information and Automation (2005) 87-92. [pdf]
[Sabhnani 2007a]
Sabhnani M, Dubrawski A, Schneider J. Multivariate time series
analyses using primitive univariate algorithms. Advances in Disease
Surveillance 4 (2007) 112. [pdf]
[Sabhnani 2007b] Sabhnani MR, Moore AW, and Dubrawski A. Rapid
processing of ad-hoc queries against large sets of time series.
Advances in Disease Surveillance 2 (2007) 66. [pdf]
[Sahin 2009] Sahin I,
Tsui FC.
Evaluation of the Bayesian aerosol release detector using the second
order
closure integrated PUFF and the fifth-generation mesoscale model (in
preparation).
[Sarkar
2005] Sarkar P, Moore A. Dynamic social network analysis using latent
space
models. ACM SIGKDD Explorations
Newsletter 7
(2005) 31-40. [pdf]
[Sarkar
2008] Sarkar P, Chen L, Dubrawski A. Dynamic network model for
predicting
occurrences of Salmonella at food facilities. In: Proceedings of
BioSecure
(2008) 56–63. [pdf]
[Shen
2005] Shen Y, Wong WK,
Cooper GF. Estimating the expected warning time of outbreak-detection
algorithms. In: Advances in Disease Surveillance 1 (2006) 65. [pdf]
[Shen 2006] Shen Y,
Wong WK, and Cooper GF. A generalization of the AMOC curve. Advances in
Disease Surveillance 1 (2006) 65 [pdf]
[Shen 2007a] Shen
Y, Cooper GF. A Bayesian biosurveillance method that models unknown
outbreak diseases. Proceedings of Intelligence and Security
Informatics: Biosurveillance (2007) 209-215. [pdf]
[Shen 2007b] Shen, Y,
Wong WK,
Levander J and Cooper GF. An outbreak detection algorithm that
efficiently
performs complete Bayesian model averaging over all possible spatial
distributions of disease. Advances in Disease Surveillance 4
(2007):113. [pdf]
[Shen 2008] Shen Y, Adamou C,
Dowling JN, Cooper GF. Estimating the joint disease outbreak-detection
time
when an automated biosurveillance system is augmenting traditional
clinical
case finding. Journal of Biomedical Informatics 41 (2008) 224-231. [
pdf]
[Shen 2009a] Shen Y,
Cooper GF. A
new prior for Bayesian anomaly detection – Application to
biosurveillance.
Methods of Information in Medicine (to appear).
[Shen
2009b] Shen Y, Cooper GF.
Bayesian modeling of
unknown diseases for biosurveillance. In: Proceedings of the Symposium
of the
American Medical Informatics Association (2009). [pdf]
[Shen
2009c] Shen Y. Bayesian
Modeling of Anomalies Due to Known and Unknown Causes. Doctoral
Dissertation,
Intelligent Systems Program, University of Pittsburgh (2009). [pdf]
[Siddiqui 2007a] Siddiqi S,
Boots B, Gordon GJ. A constraint generation approach to learning stable
linear dynamical systems. Advances in Neural Information Processing
Systems (2007). [pdf]
[Siddiqui 2007b] Siddiqi S, Boots B, Gordon GJ, Dubrawski AW. Learning
stable multivariate baseline models for outbreak detection. Advances in
Disease Surveillance 4 (2007) 266. [pdf]
[Sutovsky
2008] Sutovsky P, Cooper GF. Hierarchical explanation of inference in
Bayesian
networks that represent a population of independent agents. In:
Proceedings of
the European Conference on Artificial Intelligence (2008) 214-218. [pdf]
[Tsui
2009] Tsui FC, Dowling J.
Evaluation of emergency department reports and chief complaints for
influenza
detection using Bayesian case detector (in preparation).
[USDA
2008] USDA. Food Safety Inspection Service, U.S. Department of
Agriculture:
Data analysis for public health risk-based inspection system for
processing and
slaughter, Appendix E - Data analyses (2008). [pdf]
[Wagner 2006] Wagner
MM, Moore
AW, Aryel RM (eds.), Handbook of
Biosurveillance (Academic Press, 2006).
[Wong
2005a] Wong WK, Cooper GF,
Dash DH, Levander JD, Dowling JN, Hogan WR, Wagner MM. Bayesian
biosurveillance
using multiple data streams. Morbidity and Mortality Weekly Report
Supplement
54 (2005) 63-69. [pdf]
[Wong 2005b] Wong WK,
Cooper GF, Dash DH, Levander JD, Dowling JN, Hogan WR, and Wagner MM.
Population-wide anomaly detection. Data Mining Methods for Anomaly
Detection Workshop at the Eleventh ACM SIGKDD International Conference
on Knowledge Discovery and Data Mining (2005) 79-83. [pdf]
[Wong 2005c] Wong WK,
Moore AW, Cooper GF, Wagner MM. What's Strange About Recent Events
(WSARE): An algorithm for the early detection of disease outbreaks.
Journal of Machine Learning Research 6 (2005) 1961-1998. [pdf]
[Zhang
2008] Zhang Y, Schneider
J, Dubrawski A. Learning the semantic correlation: An alternative way
to gain
from unlabeled text. In: Proceedings of the Conference on Neural
Information
Processing Systems (NIPS) (2008) 1945-1952. [pdf]
This material is based
upon work supported by the National Science Foundation under
Grant
No.
0325581. Any opinions, findings, and conclusions or recommendations
expressed in this material are those of the author(s) and do not
necessarily reflect the views of the National Science Foundation.
