Open AccessAn open repository of real-time COVID-19 indicators
Author(s) -
Alex Reinhart,
Logan Brooks,
Maria Jahja,
Aaron Rumack,
Jingjing Tang,
Sumit Agrawal,
Wael Al Saeed,
Taylor Arnold,
Amartya Basu,
Jacob Bien,
Ángel Alexander Cabrera,
Andrew Chin,
Eu Jing Chua,
Brian Clark,
Sarah Colquhoun,
Nat DeFries,
David Farrow,
Jodi Forlizzi,
Jed Grabman,
Samuel Gratzl,
Alden Green,
George Haff,
Robin Han,
Kate Harwood,
Addison J. Hu,
Raphael Hyde,
Sangwon Hyun,
Ananya Joshi,
Jimi Kim,
Andrew Kuznetsov,
Wichada La MotteKerr,
Yeonjin Lee,
Kenneth Lee,
Zachary C. Lipton,
Michael X. Liu,
Lester Mackey,
Kathryn Mazaitis,
Daniel J. McDonald,
Phillip McGuinness,
Balasubramanian Narasimhan,
Michael P. O’Brien,
Natalia L. Oliveira,
Pratik Patil,
Adam Perer,
Collin A. Politsch,
Samyak Rajanala,
Dawn Rucker,
Chris E. Scott,
Nigam H. Shah,
Viswanathan Shankar,
James Sharpnack,
Dmitry Shemetov,
Noah Simon,
Benjamin Y. Smith,
Vishakha Srivastava,
Shuyi Tan,
Robert Tibshirani,
Elena Tuzhilina,
Ana Karina Van Nortwick,
Valérie Ventura,
Larry Wasserman,
B. A. Weaver,
Jeremy C. Weiss,
Spencer Whitman,
Kristin Williams,
Roni Rosenfeld,
Ryan J. Tibshirani
Publication year - 2021
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2111452118
Subject(s) - python (programming language) , covid-19 , computer science , phone , data science , public health , pandemic , open data , the internet , software , public health surveillance , tracking (education) , world wide web , data mining , medicine , infectious disease (medical specialty) , psychology , nursing , disease , pathology , pedagogy , linguistics , philosophy , programming language , operating system
Significance 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.