Abstract:
Objective To explore the application of exponential smoothing model in the prediction of monthly incidence of scarlet fever in Shanghai.
Methods According to the time distribution of monthly reported incidence of scarlet fever in Shanghai from 2004 to 2017, Holt-Winters exponential smoothing model was used to fit the incidence data of scarlet fever in Shanghai from 2004 to June 2017, and the optimal model was used to predict the incidence of scarlet fever in Shanghai from July to December 2017.
Results The fitting effect of Holt-Winters additive exponential smoothing model of scarlet fever monthly reported incidence after natural logarithmic transformation was relatively optimal, the R2 was 0.884, and the standardized bayesian information criterion value was −2.568. Its residual was a white noise sequence. The predicted value was basically consistent with the actual value, and the predicted accuracy for the period from July to October was better than that for the period from November to December in 2017.
Conclusion The exponential smoothing model can be used for the short term prediction of monthly incidence of scarlet fever in Shanghai,and the short-term prediction effect in four months is better.
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