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Early warning and predicting of COVID-19 using zero-inflated negative binomial regression model and negative binomial
Wanwan Zhou1, Daizheng Huang2, Qiuyu Liang3
1Department of Epidemiology and Biostatistics, Guangxi Medical University, 22 Shuangyong Road, Qingxiu District, Nanning, Guangxi, 530021, China.
BMC Infectious Diseases
|September 19, 2024
Summary
The Baidu Search Index effectively aids in early COVID-19 detection and trend prediction. Search terms evolved during the pandemic, highlighting its role as a surveillance system supplement.
Area of Science:
- Epidemiology
- Public Health
- Data Science
Background:
- Existing infectious disease surveillance systems face challenges in early outbreak detection.
- The Baidu Search Index offers a potential tool for real-time monitoring of public health trends.
Purpose of the Study:
- To investigate the utility of the Baidu Search Index for early warning and epidemic trend prediction of COVID-19.
- To analyze the correlation between search engine query volumes and COVID-19 case numbers.
Main Methods:
- Time series analysis and Spearman correlation were used to analyze daily COVID-19 cases and Baidu Search Index data for 8 keywords.
- Zero-inflated negative binomial and negative binomial regression models were employed for prediction.
Main Results:
- Baidu Search Index for keywords like "Influenza" and "Pneumonia" correlated with case increases before pathogen identification.
- Post-identification, terms such as "SARS," "Pneumonia," and "Coronavirus" showed strong correlations (0.69–0.89).
- Search data predicted COVID-19 trends with lead times of up to 15 days, with predicted cases exceeding actual counts in some regions.
Conclusions:
- The Baidu Search Index is a valuable tool for COVID-19 early warning and trend prediction, though relevant keywords change over time.
- Internet search data can significantly supplement traditional surveillance systems, especially where diagnostic delays occur or resources are scarce.
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