An open repository of real-time COVID-19 indicators.
An open repository of real-time COVID-19 indicators.
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DOI：
10.1073/pnas.2111452118
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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：
Reinhart A;Brooks L;Jahja M;Rumack A;Tang J;Agrawal S;Al Saeed W;Arnold T;Basu A;Bien J;Cabrera ÁA;Chin A;Chua EJ;Clark B;Colquhoun S;DeFries N;Farrow DC;Forlizzi J;Grabman J;Gratzl S;Green A;Haff G;Han R;Harwood K;Hu AJ;Hyde R;Hyun S;Joshi A;Kim J;Kuznetsov A;La Motte-Kerr W;Lee YJ;Lee K;Lipton ZC;Liu MX;Mackey L;Mazaitis K;McDonald DJ;McGuinness P;Narasimhan B;O'Brien MP;Oliveira NL;Patil P;Perer A;Politsch CA;Rajanala S;Rucker D;Scott C;Shah NH;Shankar V;Sharpnack J;Shemetov D;Simon N;Smith BY;Srivastava V;Tan S;Tibshirani R;Tuzhilina E;Van Nortwick AK;Ventura V;Wasserman L;Weaver B;Weiss JC;Whitman S;Williams K;Rosenfeld R;Tibshirani RJ
为了研究新冠肺炎大流行、其对社会的影响以及减少其传播的措施，研究人员需要关于大流行过程的详细数据。标准的公共卫生数据流报告不一致，经常进行意想不到的修改。他们还遗漏了人群行为的其他值得考虑的方面。我们提供了一个开放的美国COVID信号数据库，在县一级进行测量，并每天更新。这包括传统上报告的COVID病例和死亡，以及许多其他因素：流动性、社会距离、互联网搜索趋势、自我报告的症状以及已确定的医疗保险索赔中与COVID相关的活动模式。该数据库以一种通用、易于使用的格式提供所有信号，为公共卫生研究和业务决策提供支持。新冠肺炎疫情给美国带来了巨大的数据挑战。政策制定者、流行病学建模人员和卫生研究人员都需要有关大流行和相关公共行为的最新数据，最好是精细的空间和时间分辨率。CoVIDcast API是我们满足这一需求的尝试：自2020年4月开始运行，它提供对传统公共卫生监测信号(病例、死亡和住院)和许多新冠肺炎活动辅助指标的开放访问，例如从身份识别的医疗索赔数据、大规模在线调查、手机移动性数据和互联网搜索趋势中提取的信号。这些资料的地理分辨率很高(主要是县级的)，并且每天都会更新。COVIDcast API还跟踪历史数据的所有修订，允许建模师考虑许多公共卫生数据源常见的频繁修订和回填。所有数据都可以通过API以及随附的R和Python软件包以一种通用格式获得。本文描述了数据来源和信号，并举例说明了COVIDcast API中的辅助信号提供了与跟踪COVID活动相关的信息，增强了传统的公共卫生报告，并增强了研究和决策的能力。
To study the COVID-19 pandemic, its effects on society, and measures for reducing its spread, researchers need detailed data on the course of the pandemic. Standard public health data streams suffer inconsistent reporting and frequent, unexpected revisions. They also miss other aspects of a population’s behavior that are worthy of consideration. We present an open database of COVID signals in the United States, measured at the county level and updated daily. This includes traditionally reported COVID cases and deaths, and many others: measures of mobility, social distancing, internet search trends, self-reported symptoms, and patterns of COVID-related activity in deidentified medical insurance claims. The database provides all signals in a common, easy-to-use format, empowering both public health research and operational decision-making. The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.
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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：
10.1056/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
影响因子：
15.2
作者：
Jewell S;Futoma J;Hannah L;Miller AC;Foti NJ;Fox EB
通讯作者：
Fox EB
DOI：
10.1073/pnas.2007658117
发表时间：
2020-07-07
影响因子：
11.1
作者：
Bonaccorsi, Giovanni;Pierri, Francesco;Pammolli, Fabio
通讯作者：
Pammolli, Fabio
影响因子：
4.8
作者：
Doerr, Alexa J.
通讯作者：
Doerr, Alexa J.