Epidemic tracking and forecasting: Lessons learned from a tumultuous year.
Epidemic tracking and forecasting: Lessons learned from a tumultuous year.
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DOI：
10.1073/pnas.2111456118
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发表时间：
2021-12-21
影响因子：
11.1
通讯作者：
Tibshirani RJ
中科院分区：
文献类型：
--
作者：
Rosenfeld R;Tibshirani RJ
在过去的十年里，流行病预测得到了越来越多的关注，美国联邦政府内部的团体组织了各种预测挑战，包括疾病控制和预防中心(CDC)(1-3)、科学和技术政策办公室(OSTP)(4)、国防高级研究计划局(DARPA)(5)和其他地方(6、7)。2017年，在学术团体进行了几年的流感预测试验后，疾控中心决定将流感预测纳入其正常运作，包括每周公开沟通(8)和向上级通报情况。为了提供更可靠的基础设施和支持其预测需求，疾控中心于2019年指定了两个国家流感预测英才中心，其中一个设在马萨诸塞大学阿默斯特分校(https://reichlab.IO/People)和卡内基梅隆大学(https://delphi.)的一个CMU。EDU/关于/卓越中心/)。不无道理的是，在过去十年中，数字监测流在公共卫生中的重要性也有所上升，改进的疫情跟踪和预测模型是这些数据的关键应用。搜索和社交媒体趋势等数字流构成了重点的一大部分(9-14)；然而，更广泛地说，来自传统公共卫生报告之外的辅助流的数据也受到了相当大的关注(15-25)。自2012年以来，由我们两人共同领导的卡内基梅隆-德尔福小组一直在这两个新兴学科--流行病预测和构建相关辅助信号以帮助此类预测模型--方面开展工作。2020年，随着大流行的爆发，我们像许多其他团体一样，努力寻找方法，为国家应对大流行的努力做出贡献。我们最终将重点几乎完全转移到频谱的数据端，追求几个不同的方向，以建立并向公众提供各种新的in-a机器学习系，宾夕法尼亚州匹兹堡卡内基梅隆大学，邮编15213；b统计和数据科学系，卡内基梅隆大学，匹兹堡，宾夕法尼亚州15213
Epidemic forecasting has garnered increasing interest in the last decade, nurtured and scaffolded by various forecasting challenges organized by groups within the US federal government, including the Centers for Disease Control and Prevention (CDC)(1–3), Office of Science and Technology Policy (OSTP)(4), and Defense Advanced Research Projects Agency (DARPA)(5), and elsewhere (6, 7). In 2017, after several years of experimentation with flu forecasting in academic groups, the CDC decided to incorporate influenza forecasting into its normal operations, including weekly public communications (8) and briefing to higher-ups. To provide more reliable infrastructure and support for its forecasting needs, the CDC in 2019 designated two national Centers of Excellence for Influenza Forecasting, one at the University of Massachusetts at Amherst (https://reichlab. io/people) and one at Carnegie Mellon University (https://delphi. cmu. edu/about/center-of-excellence/). Not unrelatedly, the last decade has also seen a rise in the importance of digital surveillance streams in public health, with improving epidemic tracking and forecasting models being a key application of these data. Digital streams, such as search and social media trends, have constituted a large part of the focus (9–14); however, even more broadly, data from auxiliary streams that operate outside of traditional public health reporting, such as online surveys, medical devices, or electronic medical records (EMRs), have received considerable attention as well (15–25). The Carnegie Mellon Delphi group, which the two of us colead, has worked in both of these emerging disciplines—epidemic forecasting and building relevant auxiliary signals to aid such forecasting models—since 2012. In 2020, as the pandemic broke out, we struggled like many other groups to find ways to contribute to the national efforts to respond to the pandemic. We ended up shifting our focus to nearly entirely on the data end of the spectrum, pursuing several different directions in order to build and make available to the public a variety of new in-aMachine Learning Department, Carnegie Mellon University, Pittsburgh, PA 15213; and bDepartment of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213
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影响因子：
8.5
作者：
Koppeschaar, Carl E;Colizza, Vittoria;Franco, Ana O
通讯作者：
Franco, Ana O
影响因子：
4.3
作者：
Leuba SI;Yaesoubi R;Antillon M;Cohen T;Zimmer C
通讯作者：
Zimmer C
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
影响因子：
3.8
作者：
Biggerstaff M;Johansson M;Alper D;Brooks LC;Chakraborty P;Farrow DC;Hyun S;Kandula S;McGowan C;Ramakrishnan N;Rosenfeld R;Shaman J;Tibshirani R;Tibshirani RJ;Vespignani A;Yang W;Zhang Q;Reed C
通讯作者：
Reed C
DOI：
10.1056/nejmp0900702
发表时间：
2009-05-21
期刊：
The New England journal of medicine
影响因子：
--
作者：
Brownstein JS;Freifeld CC;Madoff LC
通讯作者：
Madoff LC