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Predictions by early indicators of the time and height of yearly influenza outbreaks in Sweden

Author

Listed:
  • Andersson, Eva

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

  • Kühlmann-Berenzon, Sharon

    (Department of Epidemiology, Swedish Institute for Infectious Disease Control, Stockholm Group for Epidemic Modelling)

  • Linde, Annika

    (Department of Epidemiology, Swedish Institute for Infectious Disease Control)

  • Schiöler, Linus

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

  • Rubinova, Sandra

    (Department of Epidemiology, Swedish Institute for Infectious Disease Control)

  • Frisén, Marianne

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

Abstract

Aims: Methods for prediction of the peak of the influenza from early observations are suggested. These predictions can be used for planning purposes. Methods: In this study, new robust methods are described and applied on weekly Swedish data on influenza-like illness (ILI) and weekly laboratory diagnoses of influenza (LDI). Both simple and advanced rules for how to predict the time and height of the peak of LDI are suggested. The predictions are made using covariates calculated from data in early LDI reports. The simple rules are based on the observed LDI values while the advanced ones are based on smoothing by unimodal regression. The suggested predictors were evaluated by cross-validation and by application to the observed seasons. Results: The relation between ILI and LDI was investigated and it was found that the ILI variable is not a good proxy for the LDI variable. The advanced prediction rule regarding the time of the peak of LDI had a median error of 0.9 weeks, and the advanced prediction rule for the height of the peak had a median deviation of 28%. Conclusions: The statistical methods for predictions have practical usefulness.

Suggested Citation

  • Andersson, Eva & Kühlmann-Berenzon, Sharon & Linde, Annika & Schiöler, Linus & Rubinova, Sandra & Frisén, Marianne, 2007. "Predictions by early indicators of the time and height of yearly influenza outbreaks in Sweden," Research Reports 2007:7, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    • Handle: RePEc:hhs:gunsru:2007_007
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    File URL: http://hdl.handle.net/2077/8475
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    References listed on IDEAS

    as
    1. Frisén, Marianne, 2007. "Principles for Multivariate Surveillance," Research Reports 2007:4, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    2. Andersson, Eva & Bock, David & Frisén, Marianne, 2007. "Modeling influenza incidence for the purpose of on-line monitoring," Research Reports 2007:5, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    3. S. Hussain & R. Harrison & J. Ayres & S. Walter & J. Hawker & R. Wilson & G. Shukur, 2005. "Estimation and forecasting hospital admissions due to Influenza: Planning for winter pressure. The case of the West Midlands, UK," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(3), pages 191-205.
    4. Bock, David, 2007. "Consequences of using the probability of a false alarm as the false alarm measure," Research Reports 2007:3, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    5. Bock, David & Andersson, Eva & Frisén, Marianne, 2007. "Statistical Surveillance of Epidemics: Peak Detection of Influenza in Sweden," Research Reports 2007:6, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    6. Andersson, Eva, 2007. "Effect of dependency in systems for multivariate surveillance," Research Reports 2007:1, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    7. Frisén, Marianne, 2007. "Optimal Sequential Surveillance for Finance, Public Health, and Other Areas," Research Reports 2007:2, University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Prediction; Influenza; Outbreak;
    All these keywords.

    JEL classification:

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

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