Ray Evan L, Wang Yijin, Wolfinger Russell D, Reich Nicholas G
Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, United States.
Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, MA, United States.
Epidemics. 2025 Mar;50:100810. doi: 10.1016/j.epidem.2024.100810. Epub 2024 Dec 25.
Over the last ten years, the US Centers for Disease Control and Prevention (CDC) has organized an annual influenza forecasting challenge with the motivation that accurate probabilistic forecasts could improve situational awareness and yield more effective public health actions. Starting with the 2021/22 influenza season, the forecasting targets for this challenge have been based on hospital admissions reported in the CDC's National Healthcare Safety Network (NHSN) surveillance system. Reporting of influenza hospital admissions through NHSN began within the last few years, and as such only a limited amount of historical data are available for this target signal. To produce forecasts in the presence of limited data for the target surveillance system, we augmented these data with two signals that have a longer historical record: 1) ILI+, which estimates the proportion of outpatient doctor visits where the patient has influenza; and 2) rates of laboratory-confirmed influenza hospitalizations at a selected set of healthcare facilities. Our model, Flusion, is an ensemble model that combines two machine learning models using gradient boosting for quantile regression based on different feature sets with a Bayesian autoregressive model. The gradient boosting models were trained on all three data signals, while the autoregressive model was trained on only data for the target surveillance signal, NHSN admissions; all three models were trained jointly on data for multiple locations. In each week of the influenza season, these models produced quantiles of a predictive distribution of influenza hospital admissions in each state for the current week and the following three weeks; the ensemble prediction was computed by averaging these quantile predictions. Flusion emerged as the top-performing model in the CDC's influenza prediction challenge for the 2023/24 season. In this article we investigate the factors contributing to Flusion's success, and we find that its strong performance was primarily driven by the use of a gradient boosting model that was trained jointly on data from multiple surveillance signals and multiple locations. These results indicate the value of sharing information across multiple locations and surveillance signals, especially when doing so adds to the pool of available training data.
在过去十年中，美国疾病控制与预防中心（CDC）组织了一项年度流感预测挑战赛，其动机是准确的概率预测可以提高态势感知能力，并产生更有效的公共卫生行动。从2021/22流感季开始，这项挑战赛的预测目标基于CDC的国家医疗安全网络（NHSN）监测系统报告的住院情况。通过NHSN报告流感住院情况是在过去几年内开始的，因此该目标信号仅有有限的历史数据可用。为了在目标监测系统数据有限的情况下进行预测，我们用两个历史记录更长的信号对这些数据进行了扩充：1）ILI+，它估计患者患有流感的门诊医生就诊比例；2）一组选定医疗机构的实验室确诊流感住院率。我们的模型Flusion是一个集成模型，它将两个机器学习模型结合起来，一个是基于不同特征集使用梯度提升进行分位数回归的模型，另一个是贝叶斯自回归模型。梯度提升模型在所有三个数据信号上进行训练，而自回归模型仅在目标监测信号（NHSN住院数据）上进行训练；所有三个模型在多个地点的数据上联合训练。在流感季的每周，这些模型都会生成当前周及接下来三周每个州流感住院预测分布的分位数；集成预测通过对这些分位数预测求平均来计算。在CDC 2023/24季流感预测挑战赛中，Flusion成为表现最佳的模型。在本文中，我们研究了促成Flusion成功的因素，发现其出色表现主要得益于使用了一个在多个监测信号和多个地点的数据上联合训练的梯度提升模型。这些结果表明了跨多个地点和监测信号共享信息的价值，特别是当这样做能增加可用训练数据池时。
