Evaluating Directionally-Sensitive Multivariate Control Charts with an Application to Biosurveillance

40 Pages Posted: 13 Apr 2008 Last revised: 22 Jun 2014

Inbal Yahav

Bar-Ilan University - Graduate School of Business Administration

Galit Shmueli

Institute of Service Science, National Tsing Hua University, Taiwan

Date Written: December 11, 2007

Abstract

The main goal of biosurveillance is the early detection of disease outbreaks. Advances in technology have allowed the collection, transfer, and storage of pre-diagnostic information in addition to traditional diagnostic data. Such data carry the potential of an earlier outbreak signature. In this work we deal with monitoring multivariate time series of daily counts. Current temporal monitoring in biosurveillance is done univariately by applying control charts to each time series separately. However, monitoring via multivariate control charts has the potential of greatly reducing false alert rates and increasing true alert rates. Classical multivariate control charts are aimed at detecting shifts in the vector of means in any direction. Whereas one-side univariate control charts are easy to obtain from their two-sided counterpart, directional sensitivity in the multivariate case is non trivial. Several approaches were suggested for obtaining directionally-sensitive multivariate Shewhart chart (commonly referred to as Hotelling T2 charts). However, there has not been an extensive comparison of these methods and it is not clear which approach performs better.

In this work we compare two computational-feasible approaches suggested in the literature, namely Follmann's simple correction and Testik and Runger's (TR) quadratic programming approach. In addition to their proposed directionally sensitive Hotelling charts we derive directionally sensitive Multivariate EWMA (MEWMA) charts. We then perform an extensive analysis of the performance the four methods, where we examine model performance in terms of true and false detection, robustness to assumptions, training data length and sensitivity to data characteristics. Our results show that TR's approach performs slightly better for normally distributed data, yet Follmann's approach is more robust to normality and independence assumptions.

Suggested Citation

Yahav, Inbal and Shmueli, Galit, Evaluating Directionally-Sensitive Multivariate Control Charts with an Application to Biosurveillance (December 11, 2007). Robert H. Smith School Research Paper No. RHS 06-059. Available at SSRN: https://ssrn.com/abstract=1119279 or http://dx.doi.org/10.2139/ssrn.1119279

Inbal Yahav (Contact Author)

Bar-Ilan University - Graduate School of Business Administration ( email )

Ramat Gan
Israel
97235318913 (Phone)

HOME PAGE: http://faculty.biu.ac.il/~yahavi1

Galit Shmueli

Institute of Service Science, National Tsing Hua University, Taiwan ( email )

Hsinchu, 30013
Taiwan

HOME PAGE: http://www.iss.nthu.edu.tw

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