nips nips2005 nips2005-4 nips2005-4-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Daniel B. Neill, Andrew W. Moore, Gregory F. Cooper
Abstract: We propose a new Bayesian method for spatial cluster detection, the “Bayesian spatial scan statistic,” and compare this method to the standard (frequentist) scan statistic approach. We demonstrate that the Bayesian statistic has several advantages over the frequentist approach, including increased power to detect clusters and (since randomization testing is unnecessary) much faster runtime. We evaluate the Bayesian and frequentist methods on the task of prospective disease surveillance: detecting spatial clusters of disease cases resulting from emerging disease outbreaks. We demonstrate that our Bayesian methods are successful in rapidly detecting outbreaks while keeping number of false positives low. 1
[1] M. Kulldorff. 1999. Spatial scan statistics: models, calculations, and applications. In J. Glaz and M. Balakrishnan, eds., Scan Statistics and Applications, Birkhauser, 303-322.
[2] D. B. Neill and A. W. Moore. 2004. Rapid detection of significant spatial clusters. In Proc. 10th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 256-265.
[3] M. Kulldorff and N. Nagarwalla. 1995. Spatial disease clusters: detection and inference. Statistics in Medicine 14, 799-810.
[4] M. Kulldorff. 1997. A spatial scan statistic. Communications in Statistics: Theory and Methods 26(6), 1481-1496.
[5] D. B. Neill and A. W. Moore. 2004. A fast multi-resolution method for detection of significant spatial disease clusters. In Advances in Neural Information Processing Systems 16, 651-658.
[6] D. B. Neill, A. W. Moore, F. Pereira, and T. Mitchell. 2005. Detecting significant multidimensional spatial clusters. In Advances in Neural Information Processing Systems 17, 969-976.
[7] D. G. Clayton and J. Kaldor. 1987. Empirical Bayes estimates of age-standardized relative risks for use in disease mapping. Biometrics 43, 671-681.
[8] A. Molli´ . 1999. Bayesian and empirical Bayes approaches to disease mapping. In A. B. Lawson, et al., eds. Disease Mapping e and Risk Assessment for Public Health. Wiley, Chichester.
[9] W. Hogan, G. Cooper, M. Wagner, and G. Wallstrom. 2004. A Bayesian anthrax aerosol release detector. Technical Report, RODS Laboratory, University of Pittsburgh.
[10] D. B. Neill, A. W. Moore, M. Sabhnani, and K. Daniel. 2005. Detection of emerging space-time clusters. In Proc. 11th ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining.
[11] R. E. Gangnon and M. K. Clayton. 2000. Bayesian detection and modeling of spatial disease clustering. Biometrics 56, 922-935.
[12] A. B. Lawson and D. G. T. Denison, eds. 2002. Spatial Cluster Modelling. Chapman & Hall/CRC, Boca Raton, FL.
[13] X. Wang, R. Hutchinson, and T. Mitchell. 2004. Training fMRI classifiers to detect cognitive states across multiple human subjects. In Advances in Neural Information Processing Systems 16, 709-716.