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.
翻译:我们引入了美国州和市一级流感预测Ged 经常性单位(GRU),并尝试纳入实时流感相关互联网搜索数据。 我们发现GRU的预测误差低于当前在两周多的时间跨度内以数据驱动流感预测的最新方法。 与其他机器学习方法不同,纳入实时互联网搜索数据并不能改善GRU的预测。