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.
- Pub Date:
- November 2019
- Computer Science - Machine Learning;
- Computer Science - Computers and Society;
- Statistics - Applications;
- Statistics - Machine Learning
- Machine Learning for Health (ML4H) at NeurIPS 2019 - Extended Abstract