Andersson Eva, Kühlmann-Berenzon Sharon, Linde Annika, Schiöler Linus, Rubinova Sandra, Frisén Marianne
Statistical Research Unit, Department of Economics, Göteborg University, Göteborg, Sweden.
Scand J Public Health. 2008 Jul;36(5):475-82. doi: 10.1177/1403494808089566. Epub 2008 Jun 20.
Methods for prediction of the peak of the influenza from early observations are suggested. These predictions can be used for planning purposes.
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.
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%.
The statistical methods for predictions have practical usefulness.
提出从早期观察结果预测流感高峰的方法。这些预测可用于规划目的。
在本研究中，描述了新的稳健方法并将其应用于瑞典流感样疾病（ILI）的每周数据和流感的每周实验室诊断（LDI）。提出了关于如何预测LDI高峰时间和高度的简单及先进规则。使用从早期LDI报告数据计算的协变量进行预测。简单规则基于观察到的LDI值，而先进规则基于单峰回归平滑。通过交叉验证和应用于观察到的季节对建议的预测因子进行评估。
研究了ILI与LDI之间的关系，发现ILI变量不是LDI变量的良好替代指标。关于LDI高峰时间的先进预测规则的中位数误差为0.9周，关于高峰高度的先进预测规则的中位数偏差为28%。
预测的统计方法具有实际用途。
