Towards the Use of Neural Networks for Influenza Prediction at Multiple Spatial Resolutions

We introduce the use of a Gated Recurrent Unit (GRU) for influenza prediction at the state- and city-level in the US, and experiment with the inclusion of real-time flu-related Internet search data. We find that a GRU has lower prediction error than current state-of-the-art methods for data-driven i...

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Bibliographic Details
Published in:arXiv.org 2019-11
Main Authors: Aiken, Emily L, Nguyen, Andre T, Santillana, Mauricio
Format: Article
Language:English
Subjects:
Data search
Influenza
Machine learning
Neural networks
Real time
Online Access:Get full text
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Summary:We introduce the use of a Gated Recurrent Unit (GRU) for influenza prediction at the state- and city-level in the US, and experiment with the inclusion of real-time flu-related Internet search data. We find that a GRU has lower prediction error than current state-of-the-art methods for data-driven influenza prediction at time horizons of over two weeks. In contrast with other machine learning approaches, the inclusion of real-time Internet search data does not improve GRU predictions.
ISSN:2331-8422