Baidu Index and COVID-19 Epidemic Forecast: Evidence From China.

Jianchun Fang, Xinyi Zhang, Yang Tong, Yuxin Xia, Hui Liu, Keke Wu
Author Information
  1. Jianchun Fang: School of Economics, Zhejiang University of Technology, Hangzhou, China.
  2. Xinyi Zhang: School of Accounting, Capital University of Economics and Business, Beijing, China.
  3. Yang Tong: School of Economics, Zhejiang University of Technology, Hangzhou, China.
  4. Yuxin Xia: School of Economics, Zhejiang University of Technology, Hangzhou, China.
  5. Hui Liu: School of Economics and Management, Zhejiang Sci-Tech University, Hangzhou, China.
  6. Keke Wu: School of Economics and Management, Southwest Jiaotong University, Chengdu, China.

Abstract

With the global spread of the Coronavirus epidemic, search engine data can be a practical tool for decision-makers to understand the epidemic's trends. This article uses trend analysis data from the Baidu search engine, the most widely used in China, to analyze the public's attention to the epidemic and the demand for N95 masks and other anti-epidemic materials and information. This kind of analysis has become an important part of information epidemiology. We have analyzed the use of the keywords "Coronavirus epidemic," "N95 mask," and "Wuhan epidemic" to judge whether the introduction of real-time search data has improved the efficiency of the Coronavirus epidemic prediction model. In general, the introduction of the Baidu index, whether in-sample or out-of-sample, significantly improves the prediction efficiency of the model.

Keywords

References

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MeSH Term

COVID-19
China
Epidemics
Humans
SARS-CoV-2
Search Engine

Word Cloud

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