An open repository of real-time COVID-19 indicators

被引：38

作者：

Reinhart, Alex ^{[1
]}

Brooks, Logan ^{[2
]}

Jahja, Maria ^{[1
,2
]}

Rumack, Aaron ^{[2
]}

Tang, Jingjing ^{[3
]}

Agrawal, Sumit ^{[4
]}

Al Saeed, Wael ^{[5
]}

Arnold, Taylor ^{[6
]}

Basu, Amartya ^{[7
]}

Bien, Jacob ^{[8
]}

Cabrera, Angel A. ^{[9
]}

Chin, Andrew ^{[2
]}

Chua, Eu Jing ^{[2
]}

Clark, Brian ^{[2
]}

Colquhoun, Sarah ^{[4
]}

DeFries, Nat ^{[2
]}

Farrow, David C. ^{[4
]}

Forlizzi, Jodi ^{[9
]}

Grabman, Jed ^{[4
]}

Gratzl, Samuel ^{[2
]}

Green, Alden ^{[1
]}

Haff, George ^{[2
]}

Han, Robin ^{[9
]}

Harwood, Kate ^{[4
]}

Hu, Addison J. ^{[1
,2
]}

Hyde, Raphael ^{[4
]}

Hyun, Sangwon ^{[8
]}

Joshi, Ananya ^{[5
]}

Kim, Jimi ^{[10
]}

Kuznetsov, Andrew ^{[9
]}

La Motte-Kerr, Wichada ^{[2
]}

Lee, Yeon Jin ^{[11
,12
]}

Lee, Kenneth ^{[13
]}

Lipton, Zachary C. ^{[2
]}

Liu, Michael X. ^{[9
]}

Mackey, Lester ^{[14
]}

Mazaitis, Kathryn ^{[2
]}

McDonald, Daniel J. ^{[15
]}

McGuinness, Phillip ^{[4
]}

Narasimhan, Balasubramanian ^{[16
,17
]}

O'Brien, Michael P. ^{[4
]}

Oliveira, Natalia L. ^{[1
,2
]}

Patil, Pratik ^{[1
,2
]}

Perer, Adam ^{[9
]}

Politsch, Collin A. ^{[2
]}

Rajanala, Samyak ^{[16
]}

Rucker, Dawn ^{[5
]}

Scott, Chris ^{[4
]}

Shah, Nigam H. ^{[18
]}

Shankar, Vishnu ^{[19
]}

机构：

[1] Carnegie Mellon Univ, Dept Stat & Data Sci, Pittsburgh, PA 15213 USA

[2] Carnegie Mellon Univ, Machine Learning Dept, Pittsburgh, PA 15213 USA

[3] Carnegie Mellon Univ, Computat Biol Dept, Pittsburgh, PA 15213 USA

[4] Google LLC, Mountain View, CA 94043 USA

[5] Carnegie Mellon Univ, Comp Sci Dept, Pittsburgh, PA 15213 USA

[6] Univ Richmond, Linguist Program, Richmond, VA 23173 USA

[7] Carnegie Mellon Univ, Informat Networking Inst, Pittsburgh, PA 15213 USA

[8] Univ Southern Calif, Dept Data Sci & Operat, Los Angeles, CA 90089 USA

[9] Carnegie Mellon Univ, Human Comp Interact Inst, Pittsburgh, PA 15213 USA

[10] Univ Texas Dallas, Sch Nat Sci & Math, Richardson, TX 75080 USA

[11] Carnegie Mellon Univ, Coll Fine Arts, Pittsburgh, PA 15213 USA

[12] Carnegie Mellon Univ, Dept Psychol, Pittsburgh, PA 15213 USA

[13] Univ Calif Davis, Dept Stat, Davis, CA 95616 USA

[14] Microsoft Res New England, Cambridge, MA 02142 USA

[15] Univ British Columbia, Dept Stat, Vancouver, BC V6T 1Z4, Canada

[16] Stanford Univ, Dept Stat, Stanford, CA 94305 USA

[17] Stanford Univ, Dept Biomed Data Sci, Stanford, CA 94305 USA

[18] Stanford Univ, Dept Med, Stanford, CA 94305 USA

[19] Stanford Univ, Program Immunol, Sch Med, Stanford, CA 94305 USA

[20] Univ Washington, Dept Biostat, Seattle, WA 98195 USA

[21] Carnegie Mellon Univ, Heinz Coll Informat Syst & Publ Policy, Pittsburgh, PA 15213 USA

来源：

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA | 2021年 / 118卷 / 51期

基金：

美国安德鲁·梅隆基金会;

关键词：

open data; digital surveillance; internet surveys; medical insurance claims; MOBILITY;

D O I：

10.1073/pnas.2111452118

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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 COVID cast 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.

引用

页数：8

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