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

arXiv: https://arxiv.org/abs/1911.02673 · HTML: https://mlsanity.com/paper/1911.02673/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Nov 2019.

## Built on by (most cited first, 4 of 4)

- [A Modified PINN Approach for Identifiable Compartmental Models in Epidemiology with Applications to COVID-19](https://mlsanity.com/paper/2208.01169/) (2022)
- [Estimating the Uncertainty of Neural Network Forecasts for Influenza Prevalence Using Web Search Activity](https://mlsanity.com/paper/2105.12433/) (2021)
- [Single Model for Influenza Forecasting of Multiple Countries by Multi-task Learning](https://mlsanity.com/paper/2107.01760/) (2021)
- [Forecasting infectious disease prevalence with associated uncertainty using neural networks](https://mlsanity.com/paper/2409.01154/) (2024)

## Abstract (arXiv)

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.