2014.ISDS.Abstracts.Final.pdf


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ISDS 2014 Conference Abstracts

Google Flu Trends: Spatial Correlation with Influenza 
Emergency Department Visits

Joseph Klembczyk*1, Mehdi Jalalpour2, Scott Levin1, Raynard Washington3, Jesse M. 
Pines4, Richard Rothman1 and Andrea Dugas1

1School of Medicine, Johns Hopkins University, Baltimore, MD, USA; 2Cleveland State University, Cleveland, OH, USA; 3AHRQ, 
Rockville, MD, USA; 4George Washington University, Washington, DC, USA

Objective
To test if Google Flu Trends (GFT) is predictive of the volume of 

influenza and pneumonia emergency department (ED) visits across 
multiple United States cities.

Introduction
GFT is a surveillance tool that gathers data on local internet 

searches to estimate the emergence of influenza-like illness in a given 
geographic location in real time.3 Previously, GFT has been proven 
to strongly correlate with influenza incidence at the national and 
regional level.2,3 GFT has shown promise as an easily accessed tool 
to enhance influenza surveillance and forecasting; however, further 
geographic validation of city-level data is needed. 1,2,6

Methods
Using Healthcare Cost and Utilization Project (HCUP) data, we 

collected weekly counts of ED visits for all patients with ICD-9 codes 
for pneumonia or influenza from 2005-2011 at 19 different cities 
geographically spread throughout the US.5 Corresponding GFT data 
for cities and associated states were collected.4 We then evaluated 
the correlation between GFT and the volume of pneumonia and 
influenza-related ED visits in each city.

Results
Correlation coefficients between city-level GFT and ED visits for 

pneumonia and influenza from 19 different cities range from 0.67 to 
0.93 with a median of 0.84. Coefficients are shown geographically 
in Figure 1.

Conclusions
We demonstrate a strong correlation between city-level GFT and 

ED visits for pneumonia and influenza across numerous US cities. 
Establishing broad geographic generalizability of city-level GFT is 
key to understanding its capabilities and further integration into other 
surveillance or forecasting models.

Figure 1: Geographic representation of 19 cities and their respective correlation 
coefficients for city-level GFT and influenza and pneumonia-related ED visits.

Keywords
Google Flu Trends; data science; big data; influenza; surveillance

Acknowledgments

This work was done in collaboration with the Agency for Healthcare 
Research and Quality (AHRQ). Its contents are solely the responsibility of 
the authors and do not necessarily represent the official views of AHRQ.

References

1. Dugas, A. F., Jalalpour, M., Gel, Y., Levin, S., Torcaso, F., Igusa, T.,
et al. (2013). Influenza forecasting with google flu trends. PloS One, 
8(2), e56176.

2. Dugas, A. F., Hsieh, Y. H., Levin, S. R., Pines, J. M., Mareiniss, D.
P., Mohareb, A., et al. (2012). Google flu trends: Correlation with 
emergency department influenza rates and crowding metrics. Clinical 
Infectious Diseases : An Official Publication of the Infectious Diseases 
Society of America, 54(4), 463-469.

3. Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. 
S., & Brilliant, L. (2008). Detecting influenza epidemics using search 
engine query data. Nature, 457(7232), 1012-1014.

4. Google. Google Flu Trends. Available at: http://www.google.org/
flutrends. Accessed 15 June 2014.

5. HCUP Nationwide Emergency Department Sample (NEDS).
Healthcare Cost and Utilization Project (HCUP). 2005-2010. Agency 
for Healthcare Research and Quality, Rockville, MD. www.hcup-us.
ahrq.gov/nedsoverview.jsp

6. Pervaiz, F., Pervaiz, M., Abdur Rehman, N., & Saif, U. (2012).
FluBreaks: Early epidemic detection from google flu trends. Journal 
of Medical Internet Research, 14(5), e125.

*Joseph Klembczyk

E-mail: jklembc1@jhmi.edu    

Online Journal of Public Health Informatics * ISSN 1947-2579 * http://ojphi.org * (1):e87, 201