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

Eva Andersson (), Sharon Kühlmann-Berenzon, Annika Linde, Linus Schiöler, Sandra Rubinova and Marianne Frisén
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Eva Andersson: Statistical Research Unit, Department of Economics, School of Business, Economics and Law, Göteborg University, Postal: Statistical Research Unit, Göteborg University, Box 640, SE 40530 GÖTEBORG
Sharon Kühlmann-Berenzon: Department of Epidemiology, Swedish Institute for Infectious Disease Control, Stockholm Group for Epidemic Modelling
Annika Linde: Department of Epidemiology, Swedish Institute for Infectious Disease Control
Linus Schiöler: Statistical Research Unit, Department of Economics, School of Business, Economics and Law, Göteborg University, Postal: Statistical Research Unit, Göteborg University, Box 640, SE 40530 GÖTEBORG
Sandra Rubinova: Department of Epidemiology, Swedish Institute for Infectious Disease Control

No 2007:7, Research Reports from University of Gothenburg, Statistical Research Unit, School of Business, Economics and Law

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.

Keywords: Prediction; Influenza; Outbreak (search for similar items in EconPapers)
JEL-codes: C10 (search for similar items in EconPapers)
Pages: 16 pages
Date: 2007-01-01
New Economics Papers: this item is included in nep-for and nep-hea
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Published in Scandinavian Journal of Public Health, 2008, pages 475-482.

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Persistent link: https://EconPapers.repec.org/RePEc:hhs:gunsru:2007_007

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