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Robust outbreak surveillance of epidemics in Sweden

Author

Listed:
  • Frisén, Marianne

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

  • Andersson, Eva

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

  • Schiöler, Linus

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

Abstract

Outbreak detection is of interest for several diseases and syndromes. The aim is to detect the progressing increase in the incidence as soon as possible after the onset of the outbreak. A semiparametric method is applied to Swedish data on tularemia and influenza. The method is constructed to detect a change from a constant level to a monotonically increasing incidence. If seasonal effects are present, the residuals from a model incorporating this can be used. The properties of the method are evaluated by application to Swedish influenza data on tularemia and influenza and by simulations. The suggested method is compared with subjective judgments and with other algorithms. The conclusion is that the method works well. A user-friendly computer program is described.

Suggested Citation

  • Frisén, Marianne & Andersson, Eva & Schiöler, Linus, 2007. "Robust outbreak surveillance of epidemics in Sweden," Research Reports 2007:12, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
  • Handle: RePEc:hhs:gunsru:2007_012
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    Citations

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    Cited by:

    1. Jonsson, Robert, 2008. "When does Heckman’s two-step procedure for censored data work and when does it not?," Research Reports 2008:2, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    2. Frisén, Marianne & Andersson, Eva & Schiöler, Linus, 2009. "Sufficient reduction in multivariate surveillance," Research Reports 2009:2, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    3. Frisén, Marianne, 2008. "Introduction to financial surveillance," Research Reports 2008:1, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    4. Schiöler, Linus, 2009. "Explorative analysis of spatial patterns of influenza incidences in Sweden 1999—2008," Research Reports 2008:5, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    5. Schiöler, Linus & Frisén, Marianne, 2008. "On statistical surveillance of the performance of fund managers," Research Reports 2008:4, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    6. Schiöler, Linus, 2010. "Modelling the spatial patterns of influenza incidence in Sweden," Research Reports 2010:1, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    7. Linus Schiöler & Marianne Frisén, 2012. "Multivariate outbreak detection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(2), pages 223-242, April.
    8. Andersson, Eva, 2008. "Hotelling´s T2 Method in Multivariate On-line Surveillance. On the Delay of an Alarm," Research Reports 2008:3, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    9. Jonsson, Robert, 2011. "A Cusum Procedure For Detection Of Outbreaks In Poisson Distributed Medical Health Events," Research Reports 2010:4, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    10. Frisén, Marianne & Andersson, Eva, 2008. "Semiparametric surveillance of outbreaks," Research Reports 2007:11, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.

    More about this item

    Keywords

    Computer program; Exponential family; Influenza; Monitoring; Ordered regression; Subjective judgment; Tularemia;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

    Statistics

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