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Exploring the Lagged Correlation Between Baidu Index and Influenza-Like Illness - China, 2014-2019
Xuan Han1, Jiao Yang1, Yan Luo1
1School of Population Medicine and Public Health, Chinese Academy of Medical Sciences (CAMS) & Peking Union Medical College, Beijing, China.
China CDC Weekly
|July 5, 2024
Summary
The Baidu Index, particularly Prevention and Symptom indexes, can serve as an early warning system for influenza epidemics in China. These search trends show a lagged correlation with influenza-like illness percentages, aiding in timely public health responses.
Area of Science:
- Epidemiology
- Public Health Surveillance
- Digital Epidemiology
Background:
- Influenza-like illness (ILI) poses a significant public health challenge.
- Traditional surveillance methods can have reporting delays.
- Digital data sources, like search engine queries, offer potential for real-time monitoring.
Purpose of the Study:
- To investigate the lagged correlation between Baidu Index search data and ILI percentages in China.
- To evaluate the potential of Baidu Index as an early warning tool for influenza epidemics.
Main Methods:
- Collected ILI% and Baidu Index data from 30 provincial-level administrative divisions (PLADs) in China (April 2014-March 2019).
- Categorized Baidu Index into Overall, Ordinary, Prevention, Symptom, and Treatment indexes based on search themes.
- Utilized the cross-correlation function (CCF) method to analyze lagged correlations.
Main Results:
- Strong correlations (CCF 0.46-0.86) were found between Baidu Overall Index and ILI%, with a median lag of 0.5 days.
- Prevention and Symptom indexes showed faster responses to ILI% (median lags -9 and -0.5 days) compared to Ordinary and Treatment indexes.
- Geographical analysis indicated earlier detection of ILI trends in northern China compared to southern regions.
Conclusions:
- The Baidu Index, specifically Prevention and Symptom indexes, demonstrates significant potential for early detection of influenza epidemics.
- This digital surveillance approach can complement traditional methods for timely public health interventions.
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