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 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.
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 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.
Authors: Richard S Hopkins; Catherine C Tong; Howard S Burkom; Judy E Akkina; John Berezowski; Mika Shigematsu; Patrick D Finley; Ian Painter; Roland Gamache; Victor J Del Rio Vilas; Laura C Streichert Journal: Public Health Rep Date: 2017 Jul/Aug Impact factor: 2.792
Authors: R A Morbey; S Harcourt; R Pebody; M Zambon; J Hutchison; J Rutter; H Thomas; G E Smith; A J Elliot Journal: Epidemiol Infect Date: 2017-04-17 Impact factor: 4.434
Authors: Elaine O Nsoesie; John S Brownstein; Naren Ramakrishnan; Madhav V Marathe Journal: Influenza Other Respir Viruses Date: 2013-12-23 Impact factor: 4.380