Predictions by early indicators of the time and height of the peaks of yearly influenza outbreaks in Sweden.
OBJECTIVE
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 to 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 relationship 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.
Statistical Research Unit, Department of Economics, Göteborg University, Göteborg, Sweden.
Eva
E
Andersson
Sharon
S
Kühlmann-Berenzon
Annika
A
Linde
Linus
L
Schiöler
Sandra
S
Rubinova
Marianne
M
Frisén
D003625:Data Collection; D004196:Disease Outbreaks; D006801:Humans; D007251:Influenza, Human / Q000175:diagnosis / Q000453:epidemiology*; D011634:Public Health; D012044:Regression Analysis; D012621:Seasons; D018571:Sentinel Surveillance; D013548:Sweden / Q000453:epidemiology; D013997:Time Factors