Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States.
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DOI：
10.1073/pnas.2113561119
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发表时间：
2022-04-12
影响因子：
11.1
通讯作者：
中科院分区：
文献类型：
--
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本文比较了在美国疫情爆发的头一年半期间，对COVID-19导致的报告死亡人数的短期预测的概率准确性。结果显示，独立模型之间和内部的准确性差异很大，集合模型的准确性更一致，该模型结合了所有符合条件的模型的预测。这表明，集成模型提供了一种可靠且相对准确的方法来预测COVID-19大流行期间的死亡人数，其性能超过了所有模型。这项工作加强了综合多个模型以支持公共卫生行动的证据基础。对美国COVID-19疫情发展轨迹的短期概率预测已成为科学建模界与公众和决策者之间可见的重要沟通渠道。预测模型提供具体的，定量的和可评估的预测，为短期决策提供信息，如医疗人员需求，学校关闭和医疗用品的分配。从2020年4月开始，美国COVID-19预测中心（https：//covid19forecasthub.org/）收集、传播和综合了来自90多个不同学术、行业和独立研究团体的数千万个具体预测。一项多模型集合预测每周结合数十个团体的预测，提供了2020年4月至2021年10月期间州和国家层面因COVID-19导致的事件死亡的最一致准确的概率预测。27个独立模型在今年全年一致提交了对COVID-19死亡人数的完整预测，其表现显示，不同时间、地理空间单位和预测范围的预测技能存在很大差异。三分之二的评估模型显示出比原始基线模型更好的准确性。随着模型对未来的预测进一步深入，预测准确性下降，20周范围内的概率误差比1周范围内的预测大3到5倍。该项目强调了政府公共卫生机构、学术建模团队和行业合作伙伴之间的合作和积极协调在开发现代建模能力以支持地方、州和联邦应对疫情方面可以发挥的作用。
This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the United States. Results show high variation in accuracy between and within stand-alone models and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public-health action. Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks.
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影响因子：
4.1
作者：
Leutbecher, M.;Palmer, T. N.
通讯作者：
Palmer, T. N.
影响因子：
15.8
作者：
Pollett S;Johansson MA;Reich NG;Brett-Major D;Del Valle SY;Venkatramanan S;Lowe R;Porco T;Berry IM;Deshpande A;Kraemer MUG;Blazes DL;Pan-Ngum W;Vespigiani A;Mate SE;Silal SP;Kandula S;Sippy R;Quandelacy TM;Morgan JJ;Ball J;Morton LC;Althouse BM;Pavlin J;van Panhuis W;Riley S;Biggerstaff M;Viboud C;Brady O;Rivers C
通讯作者：
Rivers C
影响因子：
56.9
作者：
Krishnamurti, TN;Kishtawal, CM;Surendran, S
通讯作者：
Surendran, S
影响因子：
16.6
作者：
Bracher J;Wolffram D;Deuschel J;Görgen K;Ketterer JL;Ullrich A;Abbott S;Barbarossa MV;Bertsimas D;Bhatia S;Bodych M;Bosse NI;Burgard JP;Castro L;Fairchild G;Fuhrmann J;Funk S;Gogolewski K;Gu Q;Heyder S;Hotz T;Kheifetz Y;Kirsten H;Krueger T;Krymova E;Li ML;Meinke JH;Michaud IJ;Niedzielewski K;Ożański T;Rakowski F;Scholz M;Soni S;Srivastava A;Zieliński J;Zou D;Gneiting T;Schienle M;List of Contributors by Team
通讯作者：
List of Contributors by Team
影响因子：
5.7
作者：
Lerch, Sebastian;Thorarinsdottir, Thordis L.;Gneiting, Tilmann
通讯作者：
Gneiting, Tilmann