Forecasting Influenza Based on Autoregressive Moving Average and Holt-Winters Exponential Smoothing Models
Guohun Zhu*1,*2, Liping Li*3,*4, Yuebin Zheng*1, Xiaowei Zhang*3,*4, and Hui Zou*1
*1School of EEE, Guilin University of Electronic Technology
No.1 Jinji Road, Guilin, Guangxi 541004, China
*2School of ITEE, The University of Queensland
78-626 General Purpose South Building, St Lucia, Queensland 4067, Australia
*3School of Public Health, Shantou University
No.22 Xinling Road, Shantou, Guangdong 515041, China
*4Shantou University Medical College
No.22 Xinling Road, Shantou, Guangdong 515041, China
Influenza outbreaks can be effectively prevented if further outbreaks are predicted as early as possible. This article proposes an autoregressive integrated moving average (ARIMA) model and a Holt-Winters exponential smoothing (HWES) model to analyze tweet data for predicting influenza outbreaks and to visualize the number of flu-infection-related tweets with heat maps. First, textual influenza data for Australia from June 2015 to June 2017 are collected through the Twitter Application Programming Interface (API). Next, the ARIMA and HWES models are applied to predict the difference between the flu tweets and confirmations from the Centers for Disease Control and Prevention. Finally, a visualized heat map based on influenza topics validates the modeling analysis in two different time zones. The results show that the average relative error of the ARIMA (HWES) model is 7.25% (11.29%) for the one-week flu forecast.
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