A Bayesian Hierarchical Model for Estimating Influenza Epidemic Severity

Authors

  • Nicholas L. Michaud
  • Jarad Niemi

DOI:

https://doi.org/10.5210/ojphi.v8i1.6438

Abstract

We present a model for forecasting influenza severity which uses historic and current data from both ILINet and Google Flu Trends. The model takes advantage of the accuracy of ILINet data and the real-time updating of Google Flu Trends data, while also accounting for potential bias in Google Flu Trends data. Using both data sources allows the model to more accurately forecast important characteristics of influenza outbreaks than using ILINet data alone.

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Published

2016-03-24

How to Cite

Michaud, N. L., & Niemi, J. (2016). A Bayesian Hierarchical Model for Estimating Influenza Epidemic Severity. Online Journal of Public Health Informatics, 8(1). https://doi.org/10.5210/ojphi.v8i1.6438

Issue

Vol. 8 No. 1 (2016)

Section

Oral Presentations

License

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