Influenza Forecasting with Google Flu Trends

Authors

  • Andrea F. Dugas Johns Hopkins University
  • Mehdi Jalalpour Johns Hopkins University
  • Yulia Gel Johns Hopkins University
  • Scott Levin Johns Hopkins University
  • Fred Torcaso Johns Hopkins University
  • Takeru Igusa Johns Hopkins University
  • Richard Rothman Johns Hopkins University

DOI:

https://doi.org/10.5210/ojphi.v5i1.4470

Abstract

We sought to develop a practical influenza forecast model, based on real-time, geographically focused, and easy to access data, to provide individual medical centers with advanced warning of the number of influenza cases, thus allowing sufficient time to implement an intervention. Secondly, we evaluated how the addition of a real-time influenza surveillance system, Google Flu Trends, would impact the forecasting capabilities of this model. The final model selection demonstrated that autoregression of influenza cases provides a strong base forecast model, which is enhanced by the addition of Google Flu Trends confirming the predictive capabilities of search query based syndromic surveillance.

Author Biography

Andrea F. Dugas, Johns Hopkins University

Andrea Dugas, MD, is an assistant professor in the Department of Emergency Medicine at Johns Hopkins University. After completing her Emergency Medicine training, she joined Johns Hopkins University where she is currently a practicing emergency medicine physician and PhD candidate at the Johns Hopkins Bloomberg School of Public Health. Dr. Dugas's current research focus is the detection and management of influenza in the emergency department and clinical applications of surveillance.

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Published

2013-03-23

How to Cite

Dugas, A. F., Jalalpour, M., Gel, Y., Levin, S., Torcaso, F., Igusa, T., & Rothman, R. (2013). Influenza Forecasting with Google Flu Trends. Online Journal of Public Health Informatics, 5(1). https://doi.org/10.5210/ojphi.v5i1.4470

Issue

Vol. 5 No. 1 (2013)

Section

Oral Presentations: Influenza Surveillance Methods - Research

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