Author(s)

Yan Bu¹, Jinhong Bai¹, Zhuo Chen^1,2, Mingjing Guo^1*, Fan Yang¹

¹School of Economy and Management, China University of Geosciences, Wuhan, China.
²School of Business, Central South University, Changsha, China.

Influenza is a kind of infectious disease, which spreads quickly and widely. The outbreak of influenza has brought huge losses to society. In this paper, four major categories of flu keywords, “prevention phase”, “symptom phase”, “treatment phase”, and “commonly-used phrase” were set. Python web crawler was used to obtain relevant influenza data from the National Influenza Center’s influenza surveillance weekly report and Baidu Index. The establishment of support vector regression (SVR), least absolute shrinkage and selection operator (LASSO), convolutional neural networks (CNN) prediction models through machine learning, took into account the seasonal characteristics of the influenza, also established the time series model (ARMA). The results show that, it is feasible to predict influenza based on web search data. Machine learning shows a certain forecast effect in the prediction of influenza based on web search data. In the future, it will have certain reference value in influenza prediction. The ARMA(3,0) model predicts better results and has greater generalization. Finally, the lack of research in this paper and future research directions are given.

Data Mining, Web Search, Machine Learning, Baidu Index, Influenza Prediction

Bu, Y. , Bai, J. , Chen, Z. , Guo, M. and Yang, F. (2018) The Study on China’s Flu Prediction Model Based on Web Search Data. Journal of Data Analysis and Information Processing, 6, 79-92. doi: 10.4236/jdaip.2018.63006.