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Multivariate outbreak detection

  • Schiöler, Linus

    ()

    (Statistical Research Unit, Department of Economics, School of Business, Economics and Law, Göteborg University)

  • Frisén, Marianne

    ()

    (Statistical Research Unit, Department of Economics, School of Business, Economics and Law, Göteborg University)

On-line monitoring is needed to detect outbreaks of diseases like influenza. Surveillance is also needed for other kinds of outbreaks, in the sense of an increasing expected value after a constant period. Information on spatial location or other variables might be available and may be utilized. We adapted a robust method for outbreak detection to a multivariate case. The relation between the times of the onsets of the outbreaks at different locations (or some other variable) was used to determine the sufficient statistic for surveillance. The derived maximum likelihood estimator of the outbreak regression was semi-parametric in the sense that the baseline and the slope were non-parametric while the distribution belonged to the exponential family. The estimator was used in a generalized likelihood ratio surveillance method. The method was evaluated with respect to robustness and efficiency in a simulation study and applied to spatial data for detection of influenza outbreaks in Sweden.

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File URL: http://hdl.handle.net/2077/23390
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Paper provided by Statistical Research Unit, Department of Economics, School of Business, Economics and Law, University of Gothenburg in its series Research Reports with number 2010:2.

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Length: 22 pages
Date of creation: 17 Sep 2010
Publication status: Forthcoming as Schiöler, Linus and Marianne Frisén, 'Multivariate outbreak detection' in Journal of Applied Statistics, 2011.
Handle: RePEc:hhs:gunsru:2010_002
Contact details of provider: Postal:
Statistical Research Unit, University of Gothenburg, Box 640, SE 40530 GÖTEBORG

Web page: http://www.statistics.gu.se/

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  1. Marianne Frisen & Eva Andersson & Linus Schioler, 2010. "Evaluation of multivariate surveillance," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(12), pages 2089-2100.
  2. Christian Sonesson & David Bock, 2003. "A review and discussion of prospective statistical surveillance in public health," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 166(1), pages 5-21.
  3. Andrew Lawson & Allan Clark & Carmen Vidal Rodeiro, 2004. "Developments in General and Syndromic Surveillance for Small Area Health Data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(8), pages 951-966.
  4. Zhou, Qin & Luo, Yunzhao & Wang, Zhaojun, 2010. "A control chart based on likelihood ratio test for detecting patterned mean and variance shifts," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1634-1645, June.
  5. Martin Kulldorff, 2001. "Prospective time periodic geographical disease surveillance using a scan statistic," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 61-72.
  6. Frisén, Marianne & Andersson, Eva & Schiöler, Linus, 2007. "Robust outbreak surveillance of epidemics in Sweden," Research Reports 2007:12, Statistical Research Unit, Department of Economics, School of Business, Economics and Law, University of Gothenburg.
  7. Peter A. Rogerson, 2001. "Monitoring point patterns for the development of space-time clusters," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 164(1), pages 87-96.
  8. Höhle, Michael & Paul, Michaela, 2008. "Count data regression charts for the monitoring of surveillance time series," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4357-4368, May.
  9. Bersimis, Sotiris & Psarakis, Stelios & Panaretos, John, 2006. "Multivariate Statistical Process Control Charts: An Overview," MPRA Paper 6399, University Library of Munich, Germany.
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