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  4. Vol 5, Issue 2, 2025

Research on the Application of Social Media and Search Engine Big Data in Forecasting Major Infectious Diseases

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DOI: 10.23977/phpm.2025.050213 | Downloads: 2 | Views: 1320

Author(s)

Zongjing Liang 1, Zhijie Li 1, Yun Kuang 2

Affiliation(s)

1 School of Economics and Management, Guangxi Normal University, Guilin, Guangxi, China
2 Library, Guilin Normal University, Guilin, Guangxi, China

Corresponding Author

Yun Kuang

ABSTRACT

This paper constructs a major infectious disease epidemic prediction model based on social media and search engine big data. The research object is the daily new infections of COVID-19 in China during the secondary infection peak in 2020 and 2022. The data time range is January 20, 2020-April 30, 2020 and January 2, 2022-December 25, 2022. The constructed model is the autoregressive distributed lag model (ARDL). The model dependent variable is the daily new infections, and the independent variables are Baidu Index, Weibo, news and video releases. Empirical results: The short-term effect equation shows that the information dissemination platform (such as video and Baidu ) has a significant short-term impact on the prediction of the number of infections, reflecting that the public's behavior of obtaining and disseminating epidemic information through these channels has a more direct impact on the number of epidemics. The long-term cointegration equation shows that the long-term impact of video releases in 2022 has significantly increased, indicating that video platforms have played an increasingly important role in the long-term dissemination of epidemic information predictions and may become an important source of information for the public to understand and respond to the epidemic in the long term. The empirical results show that Baidu Index, Weibo, news, and video releases all play a positive role in predicting the number of new infections, but their effects vary. The conclusions of this study can provide a new research paradigm for the prediction of major infectious diseases that may occur again in the future.

KEYWORDS

COVID-19; ARDL Model; Social Media Big Data; Search Engine Big Data; Predictive Analysis

CITE THIS PAPER

Zongjing Liang, Zhijie Li, Yun Kuang, Research on the Application of Social Media and Search Engine Big Data in Forecasting Major Infectious Diseases. MEDS Public Health and Preventive Medicine (2025) Vol. 5: 89-94. DOI: http://dx.doi.org/10.23977/phpm.2025.050213.

REFERENCES

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