Data - Climatological specific humidity for HHS regions from Evaluation of mechanistic and statistical methods in forecasting influenza-like illness

A variety of mechanistic and statistical methods to forecast seasonal influenza have been proposed and are in use; however, the effects of various data issues and design choices (statistical versus mechanistic methods, for example) on the accuracy of these approaches have not been thoroughly assessed. Here, we compare the accuracy of three forecasting approaches—a mechanistic method, a weighted average of two statistical methods and a super-ensemble of eight statistical and mechanistic models—in predicting seven outbreak characteristics of seasonal influenza during the 2016–2017 season at the national and 10 regional levels in the USA. For each of these approaches, we report the effects of real time under- and over-reporting in surveillance systems, use of non-surveillance proxies of influenza activity and manual override of model predictions on forecast quality. Our results suggest that a meta-ensemble of statistical and mechanistic methods has better overall accuracy than the individual methods. Supplementing surveillance data with proxy estimates generally improves the quality of forecasts and transient reporting errors degrade the performance of all three approaches considerably. The improvement in quality from ad hoc and post-forecast changes suggest that domain experts continue to possess information that is not being sufficiently captured by current forecasting approaches.

学界已提出并投入使用多种用于季节性流感（seasonal influenza）预测的机理模型（mechanistic method）与统计方法（statistical method），但各类数据问题与建模设计选择（例如统计方法与机理模型的差异）对这类预测方法精度的影响，尚未得到全面评估。本研究针对三种预测方案展开精度对比：分别为单种机理模型、两种统计方法的加权平均模型，以及由8种统计与机理模型构成的超级集成模型（super-ensemble）；并以此预测2016-2017流感季美国全国及10个地区层面的7项流感暴发特征。针对上述三种方案，本研究分别评估了监测系统的实时漏报与虚报情况、流感活动非监测替代指标的使用，以及人工干预模型预测结果对预测质量的影响。本研究结果显示，由统计与机理方法构成的元集成模型（meta-ensemble）整体精度优于单一模型。通过替代指标估算值补充监测数据，通常可提升预测质量；而临时性报告误差则会显著降低三种方法的预测性能。通过临时调整及预测后修正实现的预测质量提升，表明领域专家仍掌握着当前预测方法尚未充分捕捉的信息。