A Collaborative Multi-Model Ensemble for Real-Time Influenza Season Forecasting in the U.S
A Collaborative Multi-Model Ensemble for Real-Time Influenza Season Forecasting in the U.S
复制标题
用于美国实时流感季节预测的协作多模型集成
DOI：
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复制
发表时间：
2019
期刊：
影响因子：
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通讯作者：
J. Shaman
中科院分区：
文献类型：
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作者：
N. Reich;Craig J. McGowan;T. Yamana;A. Tushar;E. Ray;D. Osthus;S. Kandula;L. Brooks;Willow Crawford;G. Gibson;Evan Moore;Rebecca Silva;M. Biggerstaff;M. Johansson;Roni Rosenfeld;J. Shaman
季节性流感在美国和世界范围内每年导致大量的发病率和死亡率。准确预测流感流行的关键特征，如特定季节发病高峰的时间和严重程度，可以为公共卫生应对疫情提供信息。作为将数据和先进分析方法纳入公共卫生决策的持续努力的一部分，美国疾病控制和预防中心（CDC）自2013/2014季节以来组织了季节性流感预测挑战。在2017/2018赛季，有22支球队参加。2017年初，由四个团队组成的小组创建了一个名为FluSight Network的研究联盟。在2017/2018赛季，他们共同努力，制作了一个协作的多模型集合，使用称为堆叠的机器学习技术将21个单独的组件模型组合成一个模型。该方法创建预测密度的加权平均值，其中每个分量的权重基于该分量在过去季节中的预测准确度。在2017/2018年流感季节，这是过去15年来最大的季节性疫情之一，这种多模型集合的平均表现优于所有单个组件模型，并在CDC挑战中排名第二。它也优于CDC创建的基线多模型集合，该集合对提交给预测挑战的所有模型进行简单平均。该项目表明，研究小组之间的合作努力，以开发集合预报方法可以带来可衡量的改进，在预测的准确性和重大减少的变化性能从一年到一年。诸如此类的努力强调对预测模型的实时测试和评估，并促进公共卫生官员和建模研究人员之间的密切合作，对于提高我们对如何最好地利用预测来改善公共卫生应对季节性和新出现的流行病威胁的理解至关重要。
Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is based on that component’s forecast accuracy in past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats.
影响因子：
3.8
作者：
Viboud C;Sun K;Gaffey R;Ajelli M;Fumanelli L;Merler S;Zhang Q;Chowell G;Simonsen L;Vespignani A;RAPIDD Ebola Forecasting Challenge group
通讯作者：
RAPIDD Ebola Forecasting Challenge group