Comparing Directionally Sensitive MCUSUM and MEWMA Procedures with Application to Biosurveillance

Fricker, Ronald D.; Knitt, Matthew C.; Hu, Cecilia X.

doi:10.1080/08982110802334104

Cited by 26 publications

(24 citation statements)

References 22 publications

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“…Jackson et al 37 compared the performance of EMWA, Shewhart chart, and a general linear model method based on the data set having day-of-the-week effects. Because of the spatiotemporal characteristics of biosurveillance data, modifications of the multivariate SPC techniques such as MCUSUM and MEWMA have been suggested by Joner et al 35 and Fricker et al 38 Comprehensive review of the applications of SPC methods to health surveillance can be found in Woodall 1 and Tsui et al 4 .…”

Section: Introductionmentioning

confidence: 98%

A comparison of CUSUM, EWMA, and temporal scan statistics for detection of increases in poisson rates

Han

Tsui

Ariyajunya

et al. 2009

Quality & Reliability Eng

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Section: Introductionmentioning

confidence: 98%

A comparison of CUSUM, EWMA, and temporal scan statistics for detection of increases in poisson rates

Han

Tsui

Ariyajunya

et al. 2009

Quality & Reliability Eng

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“…Often the goal in applications such as biosurveillance is to detect "signed" change, such as increases in reported incidents of an infectious disease. Although several approaches to developing directionally sensitive multivariate procedures have been considered (Fricker 2007;Fricker, Knitt, and Hu 2008;Joner et al 2008), existing graph-theoretic procedures are not designed for this setting.…”

Section: Future Workmentioning

confidence: 99%

Nonparametric Tests for Homogeneity Based on Non-Bipartite Matching

Ruth

Koyak

2011

Journal of the American Statistical Association

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Given a sequence of observations, has a change occurred in the underlying probability distribution with respect to observation order? This problem of detecting change points arises in a variety of applications including health prognostics for mechanical systems, syndromic disease surveillance in geographically dispersed populations, anomaly detection in information networks, and multivariate process control in general. Detecting change points in high-dimensional settings is challenging, and most change-point methods for multidimensional problems rely upon distributional assumptions or the use of observation history to model probability distributions. We present three new nonparametric statistical tests for heterogeneity based on the combinatorial properties of minimum non-bipartite matching (MNBM). The key idea underlying each of these tests is that if a sequence of independent random observations undergoes a change in distribution-either an abrupt "shift" or a gradual "drift"-a MNBM based on inter-point distances tends to produce pairings that are closer in the sequence labeling than would be the case if the observations were drawn from the same distribution. Our tests follow on the work of Rosenbaum (2005) who used MNBM to derive a simple cross-match test statistic for the two-sample problem based on this idea. Similar ideas are present in the minimum spanning tree (MST) test derived by Rafsky (1979, 1981). We extend these approaches by utilizing ensembles of orthogonal MNBMs which greatly increase information extraction from the data, leading to tests that compare favorably to parametric procedures while maintaining level and good power properties across distributions.

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“…We assume that such systematic components of the data can be appropriately modeled and thus accounted for and removed from the data. See, for example, Fricker et al [12,13] where adaptive regression was used to remove the systematic effects from syndromic surveillance data. We then assume that the Shewhart charts are used to monitor the standardized residuals from such a model and that the residuals can be assumed to be independently distributed according to a standard normal distribution.…”

Section: The Biosurveillance Problemmentioning

confidence: 99%

Optimizing Biosurveillance Systems that Use Threshold-based Event Detection Methods

Fricker¹,

D.²,

David³

2009

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a b s t r a c tWe describe a methodology for optimizing a threshold detection-based biosurveillance system. The goal is to maximize the system-wide probability of detecting an ''event of interest" against a noisy background, subject to a constraint on the expected number of false signals. We use nonlinear programming to appropriately set detection thresholds taking into account the probability of an event of interest occurring somewhere in the coverage area. Using this approach, public health officials can ''tune" their biosurveillance systems to optimally detect various threats, thereby allowing practitioners to focus their public health surveillance activities. Given some distributional assumptions, we derive a one-dimensional optimization methodology that allows for the efficient optimization of very large systems. We demonstrate that optimizing a syndromic surveillance system can improve its performance by 20-40%.Published by Elsevier B.V.

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Comparing Directionally Sensitive MCUSUM and MEWMA Procedures with Application to Biosurveillance

Cited by 26 publications

References 22 publications

A comparison of CUSUM, EWMA, and temporal scan statistics for detection of increases in poisson rates

A comparison of CUSUM, EWMA, and temporal scan statistics for detection of increases in poisson rates

Nonparametric Tests for Homogeneity Based on Non-Bipartite Matching

Optimizing Biosurveillance Systems that Use Threshold-based Event Detection Methods

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