Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States.
Comparing trained and untrained probabilistic ensemble forecasts of COVID-19 cases and deaths in the United States.
复制标题
DOI：
10.1016/j.ijforecast.2022.06.005
复制
发表时间：
2023-07
影响因子：
7.9
通讯作者：
Reich, Nicholas G.
中科院分区：
文献类型：
--
作者：
Ray, Evan L.;Brooks, Logan C.;Bien, Jacob;Biggerstaff, Matthew;Bosse, Nikos I.;Bracher, Johannes;Cramer, Estee Y.;Funk, Sebastian;Gerding, Aaron;Johansson, Michael A.;Rumack, Aaron;Wang, Yijin;Zorn, Martha;Tibshirani, Ryan J.;Reich, Nicholas G.
美国新冠肺炎预测中心汇总了许多贡献团队对新冠肺炎在美国短期负担的预测。我们研究了建立一个集合了这些团队的预测的方法。这些实验为The Hub使用的集成方法提供了参考。为了对政策制定者最有用，集合预报必须在成分预报的两个关键特征存在的情况下具有稳定的表现：(1)偶尔与报告的数据不一致，(2)成分预测者的相对业绩随着时间的推移不稳定。我们的结果表明，在存在这些挑战的情况下，使用所有成分预测的平均加权中值进行未经训练的稳健方法进行集成是支持公共卫生决策者的良好选择。在一些有贡献的预报员有稳定的良好业绩记录的情况下，给这些预报员更高权重的训练有素的合奏也可能是有帮助的。
The U.S. COVID-19 Forecast Hub aggregates forecasts of the short-term burden of COVID-19 in the United States from many contributing teams. We study methods for building an ensemble that combines forecasts from these teams. These experiments have informed the ensemble methods used by the Hub. To be most useful to policymakers, ensemble forecasts must have stable performance in the presence of two key characteristics of the component forecasts: (1) occasional misalignment with the reported data, and (2) instability in the relative performance of component forecasters over time. Our results indicate that in the presence of these challenges, an untrained and robust approach to ensembling using an equally weighted median of all component forecasts is a good choice to support public health decision-makers. In settings where some contributing forecasters have a stable record of good performance, trained ensembles that give those forecasters higher weight can also be helpful.
登录
查看更多内容
影响因子：
1.9
作者：
Hora, Stephen C.;Fransen, Benjamin R.;Susel, Irving
通讯作者：
Susel, Irving
DOI：
10.1089/bsp.2011.0007
发表时间：
2011-06-01
期刊：
BIOSECURITY AND BIOTERRORISM-BIODEFENSE STRATEGY PRACTICE AND SCIENCE
影响因子：
--
作者：
Lipsitch, Marc;Finelli, Lyn;Redd, Stephen C.
通讯作者：
Redd, Stephen C.
影响因子：
7.7
作者：
Lowe, Rachel;Coelho, Caio A. S.;Rodo, Xavier
通讯作者：
Rodo, Xavier
DOI：
10.1073/pnas.2111453118
发表时间：
2021-12-21
影响因子：
11.1
作者：
McDonald DJ;Bien J;Green A;Hu AJ;DeFries N;Hyun S;Oliveira NL;Sharpnack J;Tang J;Tibshirani R;Ventura V;Wasserman L;Tibshirani RJ
通讯作者：
Tibshirani RJ
DOI：
10.1073/pnas.2113561119
发表时间：
2022-04-12
影响因子：
11.1
作者：
通讯作者：
--