Mitigating the effects of disease outbreaks with timely and effective interventions requires accurate real-time surveillance and forecasting of disease activity, but traditional health care–based surveillance systems are limited by inherent reporting delays. Machine learning methods have the potential to fill this temporal “data gap,” but work to date in this area has focused on relatively simple methods and coarse geographic resolutions (state level and above). We evaluate the predictive performance of a gated recurrent unit neural network approach in comparison with baseline machine learning methods for estimating influenza activity in the United States at the state and city levels and experiment with the inclusion of real-time Internet search data. We find that the neural network approach improves upon baseline models for long time horizons of prediction but is not improved by real-time internet search data. We conduct a thorough analysis of feature importances in all considered models for interpretability purposes.

通过及时有效的干预措施减轻疾病暴发的影响需要对疾病活动进行准确的实时监测和预测，但传统的基于卫生保健的监测系统受到固有报告延迟的限制。机器学习方法有可能填补这一时间“数据空白”，但迄今为止，该领域的工作主要集中在相对简单的方法和粗略的地理分辨率（州级及以上）上。我们评估门控循环单元神经网络方法与用于估计美国州和城市级别流感活动的基线机器学习方法相比的预测性能，并尝试包含实时互联网搜索数据。我们发现神经网络方法改进了用于长期预测的基线模型，但并没有被实时互联网搜索数据改进。出于可解释性的目的，我们对所有考虑的模型中的特征重要性进行了彻底分析。