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Evaluating Google, Twitter, and Wikipedia as Tools for Influenza Surveillance Using Bayesian Change Point Analysis: A
J Danielle Sharpe1, Richard S Hopkins, Robert L Cook
1College of Public Health and Health Professions, Department of Epidemiology, University of Florida, Gainesville, FL, United States. danielle.sharpe@emory.edu.
JMIR Public Health and Surveillance
|October 22, 2016
Summary
Google searches best complement traditional influenza surveillance, showing higher sensitivity and positive predictive value than Twitter or Wikipedia for detecting influenza-like illness (ILI) changes. Further development of these web-based sources is recommended.
Area of Science:
- Public Health Surveillance
- Epidemiology
- Digital Epidemiology
Background:
- Traditional influenza surveillance relies on healthcare provider-reported influenza-like illness (ILI) data, missing individuals who do not seek medical care.
- Web-based data sources (Google, Twitter, Wikipedia) offer potential for public health surveillance by capturing early illness reporting.
- Previous studies evaluated these sources individually, necessitating a comparative analysis to determine the most effective complement to traditional ILI surveillance.
Purpose of the Study:
- To comparatively analyze Google, Twitter, and Wikipedia for their correspondence with Centers for Disease Control and Prevention (CDC) ILI data.
- To identify which web-based data source best complements traditional ILI surveillance methods.
- To test the hypothesis that Wikipedia would best correspond with CDC ILI data due to lower media influence.
Main Methods:
- Collected deidentified data from CDC, Google Flu Trends, HealthTweets, and Wikipedia for 2012-2015 influenza seasons.
- Employed Bayesian change point analysis to identify seasonal shifts in each data source.
- Compared change points in Google, Twitter, and Wikipedia against CDC ILI data (gold standard) for temporal alignment, calculating sensitivity and positive predictive values (PPV).
Main Results:
- Google demonstrated high sensitivity (92%) and PPV (85%) in detecting CDC ILI change points.
- Twitter showed lower performance with 50% sensitivity and 43% PPV.
- Wikipedia exhibited the lowest sensitivity (33%) and PPV (40%) among the evaluated web-based sources.
Conclusions:
- Google provided the best combination of sensitivity and PPV for detecting influenza-related data changes among the three web-based sources.
- While web-based data streams occasionally aligned with CDC ILI data, they did not capture all changes.
- Further research and development are needed for Google, Twitter, and Wikipedia to enhance their utility in complementing traditional influenza surveillance.
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