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Syndromic Surveillance Models Using Web Data: The Case of Influenza in Greece and Italy Using Google Trends
Loukas Samaras1, Elena García-Barriocanal1, Miguel-Angel Sicilia1
1Computer Science Department, University of Alcalá, Alcalá de Henares (Madrid), Spain.
JMIR Public Health and Surveillance
|November 22, 2017
Summary
Internet search data, specifically Google Trends, can accurately predict seasonal influenza outbreaks in Greece and Italy. This method offers a reliable way to forecast flu spread and peak times, aiding public health preparedness.
Area of Science:
- Epidemiology
- Digital Health
- Public Health Surveillance
Background:
- Internet search query data has shown a correlation with infectious disease outbreaks.
- Previous research indicates a link between online activity and disease prevalence.
Purpose of the Study:
- To establish a correlation between Google Trends data and official influenza case data in Greece and Italy.
- To examine the development and spread of seasonal influenza using online search trends.
Main Methods:
- Utilized multiple regression analysis on Google search terms related to influenza (2011-2012).
- Employed autoregressive integrated moving average (ARIMA) models to correlate search data with official influenza cases.
- Developed a flu score for Greece and compared data with Italy.
Main Results:
- Demonstrated a significant correlation between Google search data and confirmed influenza cases.
- Achieved high correlation coefficients: Greece (.909 in 2011, .831 in 2012) and Italy (.979 in 2011, .933 in 2012).
- Accurately predicted influenza peaks, providing advance forecasts.
Conclusions:
- Google search data can be leveraged to create an effective Internet surveillance system for tracking influenza.
- This system can enhance real-time monitoring of influenza in Greece and Italy.
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